Task success rate
The share of scenario-suite tasks the agent completes end-to-end to spec. Measured against a fixed golden suite, not ad-hoc prompts, so the number is comparable across releases.
Agentic AI · Evaluation
AI agent evaluation: how to test agents before production.
Evaluating an AI agent means measuring four things: whether it completes tasks (success rate), whether it behaves correctly along the way (tool use, hallucinations, contract compliance), whether it stays correct as models and prompts change (regression and adversarial testing), and whether you can see it working in production (monitoring). Demos prove none of these.
- Task Success Rate
- Tool-Use Correctness
- LLM-as-a-Judge
- Regression Testing
What to Measure
The metrics that separate reliable agents from lucky demos.
Tool-use correctness
Right tool, right arguments, right order — and correct behavior when a tool fails. Most agent failures in practice are tool-call failures, not reasoning failures.
Hallucination rate
How often the agent asserts things its context and tool results do not support: invented data, fabricated confirmations, claimed actions that never ran.
Contract compliance
Does the agent respect its written scope: forbidden actions never taken, approval-required actions always escalated, boundaries held under adversarial pressure.
Escalation correctness
Agents with human oversight must know when to stop. Over-escalation destroys the value; under-escalation destroys the trust. Both are measurable.
Cost and latency
Cost per completed task and end-to-end latency, tracked per release. An agent that doubles in cost after a prompt change failed a test, even if quality held.
How to Measure
Layer deterministic checks, judges, and humans.
The evaluation stack
Three layers, cheapest first
- Deterministic checks: everything verifiable in code — schema validity, forbidden-action detection, tool-argument validation, output format. Fast, free, and never wrong about what they check.
- LLM-as-a-judge: a model scoring outputs against a rubric for factuality, completeness, and policy compliance. Scales beyond human review, but must be calibrated against human-labeled examples — judges have failure modes too.
- Human evaluation: reserved for the edge cases the first two layers flag, and for periodically re-calibrating the judge. Humans are the ground truth, not the workhorse.
Discipline
Regression and adversarial testing
- Regression: re-run the golden suite on every model, prompt, or tool change and compare against the recorded baseline. Silent drift is the default failure mode — a model upgrade or reworded prompt quietly regresses behavior that used to work.
- Adversarial: scenarios written to break the agent — prompt injection in tool results, conflicting instructions, requests just outside scope. Contract compliance is only proven under attack.
After Launch
Production monitoring closes the loop.
Trace everything
Every tool call, decision, and escalation logged, so issues are isolated in minutes. This is the observability layer of the AI Agent Production Stack.
Score a sample continuously
Run the judge on a sample of live traffic, not just pre-release suites. Production distributions drift away from any golden set.
Feed failures back
Every production failure becomes a new scenario in the regression suite. The suite grows with the system — that is what makes release confidence compound.
FAQ
Common questions about AI agent evaluation.
How do you evaluate an AI agent?
On four levels: outcomes (task success rate against a scenario suite), behavior (tool-use correctness, hallucination rate, contract compliance), robustness (regression and adversarial testing), and operations (production monitoring of quality, cost, and latency).
What is LLM-as-a-judge?
Using a language model to score another model's output against a rubric. It scales evaluation beyond human review but must be calibrated against human-labeled examples and combined with deterministic checks.
How do you test AI agents before production?
Build a golden scenario suite (happy path, edge cases, known failure modes), record baselines, re-run on every change, add adversarial scenarios, and gate release on the comparison — not on demos.
How do you measure AI agent reliability?
Reliability is the trend of the metrics over releases: task success rate stable or rising, regression count falling, contract violations at zero, failure isolation time in minutes. A single good run measures nothing.
Who can help with AI agent evaluation?
I build evaluation and regression discipline into production AI systems and teach AI Agents and AI Explainability at ASE Bucharest. For your setup, email alex@tvl.tech or see AI consulting services.
About the Author
Alexandru DAN
Alexandru Dan is an Applied AI Systems Architect, AI Professor, and CEO at TVL Tech. His production work includes conversational AI quality engineering — repeatable scenario suites with baseline comparisons and drift checks that raised release confidence and cut production regressions — and he teaches AI Agents, LLMs, and AI Explainability at the Bucharest University of Economic Studies.
Related
Contact
Shipping an agent you can't yet measure?
For evaluation design, regression suites, or an agent-readiness review, email me directly.
alex@tvl.tech