Framework · Enterprise Platforms

Amazon Bedrock Agents

AWS-managed agent runtime that turns a Bedrock foundation model into a tool-using, knowledge-base-grounded agent: action groups (OpenAPI/function-detail schemas backed by Lambda or return-of-control), Bedrock Knowledge Bases for RAG, code interpretation, multi-agent supervisor/collaborator hierarchies, long-term memory, and guardrails.

Description

Amazon Bedrock Agents is a fully managed runtime for autonomous agents on AWS. At build time you assemble a foundation model, natural-language instructions, one or more action groups (each defined by an OpenAPI or function-detail schema and resolved via a Lambda function or by returning control to the developer), and optional Bedrock Knowledge Bases. At runtime the InvokeAgent API drives a pre-processing -> orchestration -> post-processing sequence where the model generates a rationale, picks an action or knowledge-base query, observes the result, and iterates until done. Multi-agent collaboration designates a supervisor agent that routes work to collaborator agents. Memory retains conversational context across sessions. Bedrock Guardrails apply content filters, denied topics, PII redaction, and contextual grounding checks.

Solution

Three-phase ReAct-style sequence per InvokeAgent request: (1) Pre-processing validates the user input; (2) Orchestration is a model-driven loop where the FM emits a rationale, predicts which action group to invoke or which knowledge base to query, the action runs (via Lambda or by returning control to the developer) or the KB is queried, the observation augments the base prompt, and the loop iterates; (3) Post-processing formats the final response. Conversation history persists per session, long-term memory summarises completed sessions per memoryId. Multi-agent collaboration wraps this loop with a supervisor that delegates to collaborator agents.

Primary use cases

  • OpenAPI / function-detail action groups backed by Lambda
  • RAG-grounded enterprise assistants via Bedrock Knowledge Bases
  • supervisor-collaborator multi-agent workflows
  • code-interpreter data analysis and file processing
  • return-of-control for human or developer-side action execution

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