Building Multi-Agentic AI Workflows Using Amazon Bedrock

Amazon Bedrock enables developers to integrate powerful generative AI models without the complexity of model training, infrastructure management, or scaling. You can build intelligent multi-agent applications that process and respond to user queries using advanced AI techniques efficiently.

In this Cloud Lab, you’ll begin by creating and populating a dataset in an Amazon S3 bucket. This dataset will serve as the source for hotel and restaurant information that your agents will use to generate recommendations.

Next, you’ll create two Bedrock Agents: the hotel and restaurant agents. Each agent will be configured with its own action group, which links the agent to an AWS Lambda function. These Lambda functions will be responsible for retrieving, filtering, and returning relevant results from the dataset in S3. The Hotel Agent will process queries based on city and budget, while the restaurant agent will focus on city and cuisine.

After setting up specialized agents, you’ll create a supervisor agent that coordinates requests and determines when to invoke the hotel agent, the restaurant agent, or both in parallel. This ensures that user queries are intelligently routed and handled by the appropriate agents.

Finally, you’ll integrate the supervisor agent with a simple frontend web application, enabling users to submit natural language queries and receive clear, structured recommendations. The supervisor agent will orchestrate agent collaboration, merge the results, and deliver concise responses back to the frontend in real time.

After completing this Cloud Lab, you’ll have the skills to use Amazon Bedrock to create multi-agent systems, integrate them with AWS services like S3 and Lambda, and connect them to a frontend application. You will also gain hands-on experience building agent collaboration workflows and designing a recommendation engine powered by generative AI.

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 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 tool access (action groups and Lambda): You’ll build two Bedrock Agents:

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

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

Each specialist is connected to an action group that calls an AWS Lambda function. 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 “good” looks like when you’re done

When your build is working well, it should:

• Route queries to the correct specialist agent consistently.

• Use Lambda-backed tool calls 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.