A Bedrock Agent doesn't just converse: it executes Lambda functions, queries databases, and chains decisions over your real infrastructure. Here's how to design one that doesn't break in production.
A directly invoked foundation model responds with text. A Bedrock Agent orchestrates a sequence of reasoning and action: it interprets the user's request, decides which tool (action group) it needs, invokes it, evaluates the result, and decides the next step, until producing a final response. Bedrock implements this with a ReAct-style (Reasoning + Acting) pattern on top of whichever underlying model you choose -- typically Claude in the Anthropic family for its tool-use performance.
The practical difference versus building your own function-calling loop: Bedrock manages session state, function-call parsing, retries on invocation failures, and full reasoning traceability (accessible via trace in debug mode), without you having to maintain that orchestration code.
An action group defines what the agent can do, via an OpenAPI (Swagger) schema describing the available operations, or via a simplified function definition. Each operation in the schema maps to a Lambda function that Bedrock invokes when the model decides it needs that action.
Schema design has the biggest impact on the agent's reliability. Vague descriptions produce erroneous invocations; poorly typed parameters produce parsing errors. Treat every `operationId` and its description as the most important prompt in the system -- the model decides which function to call almost exclusively based on that text.
The Lambda function behind an action group receives an event with the action name, the parameters extracted by the model, and must respond in the format the agent expects (`response.actionResponse` with a serialized body).
A common mistake: not validating that the parameters extracted by the model have the expected type and format before hitting downstream systems -- the model can hallucinate a nonexistent SKU or an invalid date format, and the Lambda must handle it with the same discipline as any untrusted user input.
Agents support configurable session memory (session attributes and memory configuration) that lets you maintain context across conversation turns without resending the full history on every invocation. For conversations that span sessions -- a support ticket resumed days later -- you can persist state in DynamoDB and re-inject it as initial context.
Bedrock supports multi-agent collaboration: a supervisor agent delegates subtasks to specialized agents (one for billing queries, another for technical support, another for logistics), each with its own set of action groups and its own model if needed. The supervisor decides who to delegate to based on detected intent, similar to a "router" pattern in microservices architectures, but decided by the model at runtime instead of static rules.
The cases that justify an Agent's complexity -- versus a simple prompt with RAG -- are ones that require executing actions with real side effects: creating a Jira ticket, updating a record in RDS, triggering a Step Functions flow, or querying multiple systems and synthesizing the answer. If the task is purely informational over static documents, a Knowledge Base without an agent is usually simpler, cheaper, and more predictable. Reserve Agents for flows where the action -- not just the answer -- is the deliverable.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
Contact Carlos Montiel