AI and agents · Quality and observability · 5 min
Evaluating AI agents: from demos to operational evidence
Evaluating an agent means reviewing result, process, security and cost under representative scenarios.
A correct answer can come from an incorrect process
An agent can be right by chance while using an unauthorized source, exceeding its budget or calling the wrong tool. That is why evaluation must measure both the final result and the execution path.
Test scenarios should include normal cases, ambiguous inputs, incomplete data and adverse conditions.
Turn signals into continuous discipline
Metrics should link quality with latency, cost, escalation rate and policy compliance. No single metric describes reliability.
Classified failures become regression sets so that each fix protects an observed behavior.
Reading the diagram
How to read this system
- 01Test cases include normal, ambiguous, incomplete and adversarial conditions.
- 02The agent produces a path as well as an answer: sources, tools, permissions, retries and exit condition.
- 03Results feed metrics for quality, robustness, compliance, cost and latency.
- 04Classified failures go back into the regression set so they do not reappear after a change.
Essential concepts
Technical terms
- Eval
- Systematic evaluation of behavior.
- Regression
- Reappearance of a fixed failure.
- Robustness
- Ability to handle variation and adversity.
Architecture
Implications and controls
Evaluation needs controlled datasets, versioned rubrics and comparable traces. Thresholds are agreed for each use case, not copied from generic benchmarks.
Risks to consider
- Optimizing a single metric.
- Insufficient coverage.
- Accepting a plausible output from an invalid process.
Checklist
- Measure process and output.
- Version cases, rubrics and configuration.
- Keep test sets separate.
- Turn incidents into regressions.
Conclusion
Mature evaluation asks whether the agent got there correctly, safely and within limits.