Agents
Agent Observability
The practice of instrumenting an AI agent so its behavior can be inspected afterward through traces, metrics, and logs covering every model call, tool call, and decision.
In depth
The practice of instrumenting an AI agent so its behavior can be inspected afterward through traces, metrics, and logs covering every model call, tool call, and decision. Agents are non-deterministic and take multi-step paths. The OpenTelemetry project notes that this makes their telemetry a feedback loop for evaluation as well as a debugging aid. A standard telemetry format, such as the OpenTelemetry GenAI semantic conventions, avoids lock-in to one vendor's tracing tool.
Primary source: opentelemetry.io
Example
A standard telemetry format, such as the OpenTelemetry GenAI semantic conventions, avoids lock-in to one vendor's tracing tool.
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FAQ
Common questions
What is Agent Observability?
The practice of instrumenting an AI agent so its behavior can be inspected afterward through traces, metrics, and logs covering every model call, tool call, and decision.
Why does Agent Observability matter for AI developers?
Agent Observability sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Agent Observability?
Developers Digest publishes tutorials and videos that cover Agents topics including Agent Observability. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
Eval Harness
The infrastructure that runs evals end to end: it feeds tasks to the model or agent, records every step, applies graders, and aggregates the results into scores.
Agents
Agent Swarm
A multi-agent pattern where many lightweight agents work on sub-tasks simultaneously without a central orchestrator.
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