A financial services company processes more than 10,000 customer inquiries every day through a multi-agent...

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A financial services company processes more than 10,000 customer inquiries every day through a multi-agent GenAI application that uses Amazon Bedrock AgentCore. The application agents invoke several custom tools. During peak usage periods, users report that the custom tools experience up to 40% failure rates. The tools perform inconsistently for different teams at the company.

A GenAI developer must implement an observability solution that provides end-to-end visibility into agent interactions and tool behavior. The solution must use built-in Amazon Bedrock capabilities and must not require custom instrumentation. The GenAI developer needs a solution that requires minimal performance overhead.

Which solution will meet these requirements?

Enable AgentCore Observability and trace collection. Use AWS X-Ray to capture distributed traces for the custom tools. Build Amazon CloudWatch dashboards to visualize metrics for errors, throttling, and latency during peak usage periods.

Use Amazon CloudWatch Container Insights to monitor the agents. Configure an AWS Lambda function to poll the Amazon Bedrock API for tool usage metrics. Configure the function to store results in CloudWatch to generate alerts.

Build a custom ETL pipeline that uses AWS Lambda functions to process Amazon CloudWatch logs from Amazon Bedrock. Store the processed data in Amazon DynamoDB. Use Amazon QuickSight to visualize cross-team performance patterns.

Enable AgentCore Observability and send trace data to Amazon CloudWatch Logs. Use a custom AWS Lambda function to extract tool performance metrics from the logs. Use Amazon Managed Grafana to visualize trends.

Explanation:

Amazon Bedrock AgentCore Observability is the best fit because the requirement is specifically for built-in Amazon Bedrock visibility into agent interactions, tool behavior, and failures without custom instrumentation. AWS documentation states that AgentCore provides built-in metrics, logs, and traces for AgentCore modular services, and that this data can be viewed in Amazon CloudWatch. AgentCore observability also supports sessions, traces, and spans, where traces represent full request-response cycles and spans represent operations inside a trace. This directly matches the need to analyze multi-agent interactions, tool invocations, latency, throttling, and error behavior during peak traffic.

Option A is also correct because Amazon Bedrock AgentCore integrates with AWS X-Ray for distributed tracing of AI agents and tools. AWS X-Ray support helps follow requests across service boundaries and identify performance bottlenecks in agent and tool execution. CloudWatch provides curated GenAI observability views for AgentCore and can show operational health, metrics, traces, sessions, and error breakdowns. This gives the developer end-to-end visibility with minimal additional overhead because the core telemetry is service-provided rather than extracted through a custom pipeline.

Option B is incorrect because Container Insights focuses on container infrastructure and would not provide native visibility into Bedrock AgentCore agent reasoning paths or tool-level traces. Polling APIs with Lambda adds custom logic and operational overhead. Option C is incorrect because a custom ETL pipeline, DynamoDB storage, and QuickSight reporting do not satisfy the “built-in Amazon Bedrock capabilities” and “minimal overhead” requirements. Option D starts correctly with AgentCore Observability, but then adds custom Lambda processing and Grafana, which introduces unnecessary custom instrumentation and processing. Therefore, option A is the most AWS-aligned solution.

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A financial services company processes more than 10,000 customer inquiries every day through a multi-agent...

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