agent-observability

You trace AI agent decisions by instrumenting your code with OpenTelemetry-based spans, recording each model interaction and tool call into a structured decision chain for full visibility.

LLM-as-Judge is an evaluation method where a language model scores the quality of another agent's output and trajectory, enabling automated assessment of agent decisions without manual human review.

You turn production agent failures into regression tests by running a quality loop that captures failed traces and eval scores, automatically converting them into test cases for continuous debugging.

Yes, this observability approach supports OpenTelemetry instrumentation, allowing you to emit a root span for each agent run and record structured traces for decision chains and tool calls.

You need observability for AI agent decision chains to gain visibility into model interactions and tool calls, enabling effective debugging, output evaluation, and continuous improvement of production agents.

Yes, you can use code-based evals alongside LLM-as-Judge scoring within the quality loop to assess agent output quality and trajectory, capturing failures for regression testing.