Raltic field guide
AI agent observability: what teams need to see
Agent observability connects execution telemetry to the workflow record. Teams need enough evidence to diagnose failures and review outcomes without pretending they can inspect hidden model reasoning.
Move from the definition to the operating decision without losing the thread.
Direct answer
AI agent observability is the ability to inspect an agent's execution state, inputs and tool interactions where available, posted outputs and artifacts, errors, evaluation results, and human review outcome. It should help a team answer what ran, what evidence was used, what failed, and whether the result was accepted. It is not the same as exposing private chain-of-thought.
Observe both the run and the workflow
Technical traces explain execution; workflow context explains whether the work mattered and was accepted.
Runtime telemetry can show starts, stops, tool calls, errors, and latency. The workflow record adds the brief, expected proof, reviewer feedback, tasks, and final decision.
Without technical telemetry, failures are difficult to diagnose. Without workflow context, a successful trace can still represent the wrong task or an unapproved result.
- 01Runtime and agent identity
- 02Start, stop, waiting, and error state
- 03Tool interactions and artifacts where available
- 04Posted evidence and uncertainty
- 05Evaluation and human review outcome
Do not confuse observability with chain-of-thought
Observe actions, evidence, outputs, and evaluations rather than demanding hidden reasoning.
Useful observability does not require a model to reveal private internal reasoning. Teams can inspect the sources selected, tools called, files changed, tests run, outputs posted, and critiques produced.
This action-and-evidence record is more operationally useful because it can be validated against the real environment.
- 01Prefer source citations over narrative confidence
- 02Prefer diffs and tests over claims of correctness
- 03Prefer explicit uncertainty over invented certainty
- 04Record reviewer feedback and revision
Connect traces to task-specific evaluations
A trace shows what happened; an evaluation tests whether the output met the workflow's requirements.
Anthropic recommends using evaluations to make agent behavior measurable. Build a small set of representative cases and score the qualities that matter: evidence accuracy, completeness, policy compliance, or code correctness.
Use production corrections and rejected outputs to improve the evaluation set. That closes the loop between observation and workflow quality.
- 01Representative tasks
- 02Clear scoring rubric
- 03Failure categories
- 04Human-reviewed examples
- 05Regression checks after workflow changes
Track operating metrics that support decisions
Measure review readiness, failure recovery, corrections, and reusable outcomes rather than token volume alone.
Latency and cost matter, but they do not prove value. Track time to a review-ready result, the rate of missing evidence, how often humans reject or materially revise output, and whether the next run can reuse the prior record.
These metrics reveal whether orchestration is reducing work or merely creating more output to inspect.
- 01Time to review-ready artifact
- 02Tool or runtime failure rate
- 03Evidence completeness
- 04Material correction rate
- 05Time to recover or hand off
- 06Reuse in later workflows
Primary sources
Product and practice claims in this guide are grounded in current first-party or standards sources. Raltic product claims are limited to shipped behavior described on this site.
FAQ