AWS Launches Agentic Retrieval on Managed Amazon Bedrock Knowledge Base

Summary:AWS has launched an agentic retrieval solution built on the Managed Amazon Bedrock Knowledge Base. This solution leverages the AgentCore Gateway and AgenticRetrieveStream API to enable multi-knowledge base semantic routing and reasoning-driven retrieval, accompanied by seven layers of observability and both on-demand and continuous evaluation capabilities. The entire system is deployed as an AWS CDK application, streamlining the process of building and maintaining complex enterprise-grade retrie

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Key Breakthroughs

AWS's newly released agentic retrieval solution, built on the Managed Amazon Bedrock Knowledge Base, features the following core innovations:

1. Agentic Retrieval: Utilizing the AgentCore Gateway and AgenticRetrieveStream API, the system enables semantic routing across multiple knowledge bases, dynamically selecting retrieval sources based on query requirements, and returning synthesized answers with cited sources.

2. Advantages of Managed Knowledge Bases: Amazon Bedrock's managed knowledge base eliminates the need for a vector database, providing end-to-end management of data ingestion, storage, indexing, and retrieval, thereby simplifying the system architecture and reducing operational costs.

3. Seven Layers of Observability: The solution integrates Amazon CloudWatch and AWS X-Ray, offering comprehensive monitoring from basic metrics to retrieval quality, token usage, and evaluation scores, ensuring transparency and traceability of system behavior.

4. On-demand and Continuous Evaluation: With built-in evaluation mechanisms, users can monitor retrieval quality in real-time and analyze system performance through continuous sampling, ensuring reliability in production environments.

Technical Mechanism Analysis

The core of this solution lies in the collaboration between the AgentCore Runtime and AgentCore Gateway. The AgentCore Runtime is responsible for receiving user queries and initiating the reasoning process of the agent, while the AgentCore Gateway acts as an intermediary, routing queries to the appropriate knowledge base tools. The AgenticRetrieveStream API executes retrieval tasks within the managed knowledge base, performing multi-round iterative retrieval and result synthesis to return a structured answer.

Architecturally, the solution is deployed using AWS CDK and consists of four main stacks: the KnowledgeBasesStack, AgenticGatewayStack, AgentRuntimeStack, and DashboardsStack. This design automatically manages cross-stack dependencies through construct references, simplifying the deployment process.

Engineering Trade-offs and Performance

• Advantages:

  • Managed knowledge bases eliminate the need for vector database operations, reducing costs.
  • Agentic Retrieval improves the quality of answers to complex queries and system flexibility.
  • Seven layers of observability provide comprehensive insights into system behavior.

• Challenges:

  • The complexity of agentic retrieval increases the difficulty of system debugging and performance optimization.
  • The continuous evaluation mechanism may introduce additional computational overhead, requiring a balance between cost and performance.

Developer Implementation and Deployment Recommendations

1. Data Preparation: Organize enterprise data into a structured knowledge base format and upload it to Amazon S3.
2. Configuration Adjustment: Adjust settings in config.py according to requirements, such as project name, region, model type, etc.
3. Deployment Process: Use the CDK CLI to execute the cdk deploy --all command, automating the deployment of all stacks.
4. Testing and Validation: Run the provided Jupyter notebook to verify the performance and observability metrics of the agentic retrieval.
5. Continuous Optimization: Adjust retrieval strategies and model parameters based on monitoring data to improve system performance.

Conclusion

AWS, through the combination of managed knowledge bases and agentic retrieval, provides a more efficient and flexible solution for enterprise-level retrieval systems. Its comprehensive observability and evaluation mechanisms ensure system reliability and maintainability, opening new possibilities for AI-driven enterprise applications.

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Source: AWS Machine Learning Blog (2026-10-26)

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Tags: #AWS Follow #Bedrock Follow #Agentic Retrieval Follow #Managed Knowledge Base Follow #Observability Follow