Building Multi-Agentic AI Workflows with Amazon Bedrock AgentCore

Building a production-ready AI agent usually means solving the same handful of problems over and over: hosting the agent's code reliably, letting it call tools and real data sources, and coordinating several agents together when one isn't enough. Amazon Bedrock AgentCore is AWS's platform for exactly that: a managed way to build, deploy, and operate AI agents at scale, in any framework, without taking on the underlying compute and infrastructure work yourself.

In this Cloud Lab, you'll use AgentCore to build a multi-agent travel planner. A Hotel Agent and a Restaurant Agent each run as their own AgentCore Runtime, reading hotel and restaurant data from an Amazon S3 bucket and answering questions about it. A Supervisor Agent, also its own Runtime, sits in front of both, deciding which specialist a user's question needs, calling it directly, and combining the results into one answer. A simple Flask frontend then puts this whole system behind a single search bar.

By the end of this Cloud Lab, you'll be comfortable deploying an AI agent as an AgentCore Runtime, connecting it to a real data source in S3, and writing your own orchestration logic to coordinate multiple agents. These are skills that carry over directly to any production multi-agent system you build on AgentCore afterward.

The following is the high-level architecture diagram of the infrastructure you’ll create in this Cloud Lab:

Why multi-agent systems are becoming the default for GenAI apps

A single model call can be great for summarization or quick Q&A, but real products usually need more: different tools, different types of reasoning, different data sources, and a predictable way to decide “what happens next.” That’s where multi-agent systems shine.

In a multi-agent setup, responsibilities are split among specialized agents (e.g., one focused on hotels and another on restaurants). A coordinating “supervisor” agent can then route the user’s request to the right specialist or invoke multiple agents in parallel, before merging the results into a single, clean answer. This pattern is especially useful for recommendation flows, support assistants, and internal copilots that need to combine multiple sources of information without turning your prompt into a giant, brittle template.

What you’re really learning in this Cloud Lab

This Cloud Lab is valuable because it’s not just “create an agent.” It walks you through the core building blocks you’ll reuse in production-style agentic systems:

• Data grounding with a simple source of truth (S3): You'll start by creating and populating a dataset in Amazon S3. Even though this dataset is intentionally simple, it mirrors a real pattern: agents are most useful when they can ground their responses in data you control.

• Specialist agents with runtimes: You’ll build two Bedrock AgentCore runtimes:

  • A Hotel Agent who handles queries based on city and budget.

  • A Restaurant Agent that handles queries based on city and cuisine.

This is the key leap from “chatbot” to “agent”: the model can decide when to use a tool, call it, and incorporate the tool’s output into the final response.

• A supervisor agent for orchestration: The supervisor agent is the system’s traffic controller. It determines whether to invoke the hotel agent, the restaurant agent, or both. Better yet, it can handle the “both” case in parallel, exactly the kind of orchestration you need when user intent spans multiple domains.

• Integration with an actual frontend (Flask): Many labs stop at the backend. This one involves integrating the supervisor agent with a simple Flask web app, enabling users to submit natural-language queries and receive structured recommendations in real time. That end-to-end loop: user input → orchestration → tool calls → merged output → UI, is what makes the experience feel production-ready.

Where this pattern shows up in real teams

If you’re building GenAI features at work, the specialist and supervisor design applies to:

• Travel or shopping recommendation engines (multi-domain retrieval and ranking).

• Customer support flows (billing agent, troubleshooting agent, policy agent).

• Internal knowledge assistants (HR agent, IT agent, engineering agent).

• “Workflow copilots” that need to chain steps (classify → retrieve → act → respond).

The takeaway: multi-agent systems are less about novelty and more about reliability. They help you keep prompts smaller, responsibilities clearer, and behavior easier to test.

What the finished build should do

When your build is working well, it should:

• Route queries to the correct specialist agent consistently to fetch relevant results from your dataset.

• Combine hotel + restaurant recommendations cleanly when a user asks for both.

• Return responses in a structured, readable format that a frontend can display.

Once you can do that with a small dataset and a simple Flask app, scaling the approach to richer data sources and more complex tools becomes a straightforward next step.