AgentOps Built with Amazon Bedrock AgentCore (2): Observability, Evaluation, and the AgentOps Lifecycle

The core of operating agentic AI is securing a different dimension of Observability compared to traditional software. While existing systems focused on infrastructure status, agents need to monitor the state of intelligence, including reasoning accuracy, hallucination, and safety.

To implement agent observability, three key signals are required.

• Traces: Record the execution trajectory step by step, from LLM inference to tool calls and result interpretation.
• Metrics: Quantify the health state of intelligence, such as goal achievement rate, tool call accuracy, and guardrail trigger frequency.
• Logs: Record contextual information such as the reasoning process (Chain-of-Thought) and detailed guardrail activation history.

AgentCore Observability is based on Amazon CloudWatch and provides a managed service that automatically collects and visualizes the entire trajectory of an agent without any code changes, through AWS Distro for OpenTelemetry (ADOT).

Additionally, based on the collected traces, an Evaluation stage follows to verify the quality of the agent. Agent evaluation is a highly advanced task that goes beyond simple input-output comparison, requiring analysis of the entire complex execution trajectory.