Build Observable Enterprise AI Agents with Amazon Bedrock
Key takeaways
- Amazon Bedrock enables building enterprise-grade agentic retrieval solutions.
- The solution supports routing across multiple knowledge bases for comprehensive answers.
- Seven layers of observability are crucial for monitoring and debugging AI agents.
- Continuous evaluation ensures the ongoing accuracy and reliability of agent responses.
Who benefits
Summary
This post details how to construct an enterprise agentic retrieval solution using Amazon Bedrock's Managed Knowledge Base and AgentCore, featuring multi-knowledge base routing and cited answers. The solution emphasizes seven layers of observability and continuous evaluation, deployable via a single AWS CloudFormation chain.
Why it matters
Professionals can build highly reliable and transparent AI agents for enterprise data, ensuring accuracy, auditability, and continuous improvement in critical business applications.
How to implement this in your domain
- 1Review the provided AWS CloudFormation template to understand the architecture and components.
- 2Configure Amazon Bedrock Knowledge Bases with your enterprise data sources for retrieval.
- 3Implement the described observability layers to monitor agent performance and user interactions.
- 4Set up continuous evaluation pipelines to ensure ongoing accuracy and relevance of agent responses.
- 5Deploy the solution using AWS CloudFormation to establish a scalable and observable agentic system.
Original post by Luis Felipe Yepez Barrios
"This post builds an enterprise agentic retrieval solution on the Amazon Bedrock Managed Knowledge Base and Amazon Bedrock AgentCore. An agent reasons, routes across multiple knowledge bases, and returns cited answers, with seven layers of observability and both on-demand and cont…"
View on XOriginally posted by Luis Felipe Yepez Barrios on X · view source
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