Multi-Agent AI Systems: Orchestrating Intelligence on Google Cloud

We have moved past the “chatbot” phase of Generative AI. The frontier is now Agentic AI—systems where models don’t just answer questions, they take action. But as tasks get more complex, a single agent often isn’t enough. It tries to do too much, loses context, or hallucinates.

The solution is a Multi-Agent System. Instead of one generalist, you build a team of specialists. One agent writes code. Another reviews it. A third writes the documentation. A “Coordinator” manages the workflow.

Google Cloud has released a comprehensive reference architecture for building these systems at scale. Here is what you need to know to build yours.

The Architecture: A Coordinator and its Specialists

At the heart of this design is the Coordinator Agent. Running on Cloud Run (for serverless scalability), this agent acts as the traffic controller. It receives the user’s request, understands the intent, and delegates work to the right sub-agents.

These interactions typically follow one of two patterns:

Sequential Flow: Agent A completes a task and passes the baton to Agent B. Perfect for linear workflows like “Extract Data -> Summarise -> Email”.
Iterative Refinement: An “Executor” agent does the work, and a “Critic” agent reviews it. If the output isn’t good enough, the Critic sends it back with feedback. This loop continues until quality standards are met.

The Secret Sauce: Interoperability

One of the biggest challenges in multi-agent systems is communication. How do you get a Python agent to talk to a tailored Vertex AI agent? Google’s architecture highlights the Agent2Agent (A2A) Protocol.

This protocol standardises how agents exchange messages, tasks, and state. It means your “team” doesn’t have to run on the same infrastructure. You could have a high-performance reasoning agent on Google Kubernetes Engine (GKE) collaborating with a lightweight tool-use agent on Vertex AI.

Building for the Enterprise

It is easy to hack together a multi-agent demo. It is hard to make it reliable enough for business critical processes. This architecture tackles the hard parts:

Human-in-the-Loop: Not every decision should be automated. The design includes specific “break points” where a human must review and approve an agent’s plan before execution.
State Management: Agents need memory. Using managed databases (like Firestore or AlloyDB) ensures that context isn’t lost if a container restarts.
Security: By isolating agents into separate Cloud Run services, you apply the principle of least privilege. The “Code Writer” agent might need internet access, but the “Database Reader” agent definitely shouldn’t.

Why Alps Agility?

We don’t just read these reference architectures; we deploy them. Alps Agility helps enterprises move from “AI experiments” to robust, agentic workflows.

Whether you need to automate a complex supply chain decision process or build a reliable customer support swarm, we have the engineering expertise to build it right.

Contact us today to start architecting your multi-agent future.

Reference: Google Cloud Architecture Center: Multi-agent AI system components and design