Observability
Last updated: 2025-10-21
Building observable multi-agent systems requires expanding the traditional pillars of logs, metrics, and traces to address the unique challenges of AI. This involves capturing specialized signals such as agent actions, tool usage, model invocations, and response patterns, to effectively debug, monitor, and optimize agent performance across key areas:
- Agent Communication: Tracking inter-agent message flows, coordination patterns, and communication bottlenecks
- Performance Monitoring: Measuring response times, resource utilization, and throughput across distributed agents
- Error Handling: Detecting failures, cascading errors, and recovery mechanisms in agent workflows
- Security & Compliance: Monitoring for unauthorized access, data leaks, and regulatory compliance across agent interactions
Evaluation-Driven Observability
Observability gives us metrics, but evaluation is the process of analyzing that data (and performing tests) to determine how well an AI agent is performing and how it can be improved. In other words, once we have traces and metrics, how we can use them to judge the agent and make decisions?
For AI evaluation strategies, see the Evaluation section.
For reference: