Building AI agents that work well in a demo is one thing, but running them in production requires serious infrastructure.
At Google Cloud Next ’26, we introduced Gemini Enterprise Agent Platform to help developers build, deploy, scale, govern, and optimize autonomous AI agents. From managing long-running state and enforcing security with the Agent Governance Stack, to orchestrating complex workflows using Agent Development Kit, these tools help you treat your agent fleet with the same rigor as your engineering organization. Here is a look back at our five-part series covering the architecture patterns and best practices you need to move your agents into production.
1. Agent design patterns for long-running AI agents
Developers spend weeks perfecting prompt engineering, tool calling, and response latency. But none of that matters when your agent loses its reasoning chain over a five-day task. At Next 26, we announced that Agent Runtime now supports long-running agents that maintain state for up to seven days.
In this article, we’ll share five essential agent design patterns for building long-running agents with Agent Runtime. You’ll learn how to implement checkpoint-and-resume mechanisms to recover from failures without starting over. We also cover how to build delegated approval workflows where the agent pauses for human review while consuming zero compute resources.
Read the full guide on long-running agents here.
2. The agent governance stack
A misconfigured SaaS tool leaks data passively, but a misconfigured agent takes bad actions actively. The pattern we saw with shadow IT in 2015 is repeating itself with AI agents.
To manage this risk, we explain why you must treat your agent fleet with the same rigor as your engineering organization. We outline a five-layer governance stack designed to provide your r security team with precise visibility and control. The foundation begins with Agent Identity, assigning every agent a unique cryptographic badge to isolate access. From there, we explore how to use Agent Registry for centralized tool governance and Agent Gateway to enforce natural language security policies across your fleet. The stack concludes with behavioral anomaly detection and a unified security dashboard to monitor your overall risk.
Read the full guide on the agent governance stack here.
3. Must-have multi-agent orchestration patterns in ADK
Building a single AI skill is relatively straightforward, but orchestrating multiple skills across different agents is notoriously difficult. With the new updates to Agent Development Kit (ADK), we introduced graph-based workflows, collaborative agents, and a formalized skills framework to solve these orchestration failures.
Our third guide details five multi-agent orchestration patterns you can use to build reliable systems. You will find code examples for building hybrid graphs that combine hard-coded business rules with flexible AI reasoning. We also show how to use the coordinator-specialist pattern to avoid building monolithic, unpredictable agents. The guide concludes with deep dives into skill composition, cross-language pipelines, and secure sandboxed executors for running arbitrary code.
Read the full guide on ADK multi-agent patterns here.
4. Deep dive: How A2A and MCP work together
Organizations will rarely build every AI agent they need entirely from scratch. The real value comes when agents built by different teams, in different languages, and across different organizations can securely discover and collaborate with each other.
In our final guide, we explore five integration patterns using the Agent-to-Agent (A2A) and Model Context Protocol (MCP) standards. You will see how Agent Cards allow agents to publish their capabilities so coordinator agents can find them through the Agent Registry. We also show how MCP acts as a universal tool bridge to connect your agents to databases and enterprise systems without custom integration code. The article finishes with strategies for cross-organization federation that involves agents from different organizations collaborating on shared tasks using the Agent Gallery in Gemini Enterprise and building ambient event meshes for agents that react to events continuously in the background, without waiting for user requests.
Read the full guide on agent interoperability here.
5. Atomic agent blueprints on Google Cloud’s Agent Garden
Building multi-agent systems from scratch presents complex design challenges, including finding the optimized design pattern for your use-case, orchestration failures and evaluation loops. You can spend weeks reinventing the wheel, trying to get your agents to be ready for production – or you can start with architectures that already work, with our new Atomic Agents in Agent Garden.
Read the full guide to learn about pre-built Agent Blueprints in Agent Garden
Watch the complete Agent Platform explainer
To see these architectural patterns in practice, watch this technical walkthrough of the Gemini Enterprise Agent Platform. This deep dive covers the complete agent lifecycle, showing you exactly how to move from initial code to a secure, scalable AI Agents in production.
Dive into the code with Agent Platform samples on GitHub
Access our curated repository of code samples and tutorials for the Gemini Enterprise Agent Platform. This GitHub repository provides practical examples for the entire agent lifecycle, giving you the exact code needed to build, scale, govern, and optimize your autonomous fleets.
Get started with Gemini Enterprise Agent Platform
Moving agents into production requires both robust infrastructure and the flexibility to choose the right reasoning engine for the task. The Gemini Enterprise Agent Platform bridges this gap, allowing you to build, govern, and scale autonomous workflows with complete enterprise control.
Through first-class integration with Model Garden, your agent fleet has direct access to more than 200 leading models. You can route tasks to the best available option, whether that is a first-party model like Gemini 3.1 Pro or Lyria 3, an open model like Gemma 4, or third-party models like Anthropic’s Claude, Opus, Sonnet or Haiku.
Visit Agent Platform in the Google Cloud console to explore new features and start building today.