Evaluating AI agents: beyond accuracy metrics
Accuracy alone doesn't tell you if an agent is production-ready. Here's the evaluation framework we use before shipping any autonomous system.
The first question most teams ask when evaluating an AI agent is: what's the accuracy? It's the wrong first question. An agent can be right 95% of the time and still be catastrophically wrong for production - if the 5% of failures are catastrophic, if the system has no recovery path, if operators can't understand why it made a decision.
After shipping agents into production environments, we've developed a five-dimension evaluation framework that determines production readiness more reliably than accuracy alone.
Dimension 1: Task completion rate
Not accuracy - completion. An agent that completes 85% of tasks correctly and gracefully escalates the other 15% is more production-ready than one that attempts 100% of tasks and is right 80% of the time. Measure what percentage of tasks the agent completes (successfully or via graceful handoff) versus what percentage it abandons, loops on, or fails silently.
Dimension 2: Failure mode quality
How does the agent fail? This is as important as whether it fails. Good failure modes: clear escalation to a human, explicit uncertainty expression, task abandonment with a diagnostic trace. Bad failure modes: confident wrong answers, silent partial execution, infinite retry loops. Actively test your agent on adversarial inputs and edge cases. The failure mode quality determines whether your agent is a liability.
Dimension 3: Consistency
Run the same input through your agent 20 times. Do you get the same output? LLMs are non-deterministic even at temperature=0 in many configurations. For decision-making agents, consistency matters - a customer support agent that gives different answers to the same question on different days erodes trust faster than one that's consistently slightly wrong.
Consistency testing has caught more pre-production issues for us than accuracy testing. An agent that's consistent but wrong is fixable via prompt or fine-tuning. An agent that's randomly right is much harder to improve.
Dimension 4: Latency distribution
Average latency is a misleading metric. P95 and P99 latency are what your users experience at the tail. An agent with 200ms average latency and 30s P99 latency will feel unreliable in production. Measure latency under load, with realistic task distributions, at peak concurrency. Build latency budgets per task type and measure against them.
Dimension 5: Explainability
Can a human understand why the agent made a specific decision? For regulated industries this is a compliance requirement. For all industries it's a debugging requirement. Build reasoning traces into your agent's output - not the full chain-of-thought (which is often verbose and misleading), but a structured summary of what information the agent used, what options it considered, and why it made the choice it did.
- Task completion rate (including graceful escalations)
- Failure mode classification - what types of failures and how the system handles them
- Consistency score across identical inputs under the same conditions
- P50/P95/P99 latency at expected production load
- Explainability - percentage of decisions with a human-readable reasoning trace
Build your eval harness to measure all five before you ship. Accuracy is one input to production readiness. By itself, it tells you almost nothing about whether real users will trust your agent with real work.
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Open the ROI calculatorFrequently asked questions
Why is accuracy not a good enough measure for a production AI agent?
Because it hides too much. An agent can be right 95% of the time and still be catastrophically wrong for production if the 5% of failures are dangerous, there is no recovery path when it fails, or operators cannot understand why it made a given decision. The five dimensions in this framework surface the problems that accuracy alone misses.
What is a realistic task completion rate to aim for before going live?
It depends on the stakes of the task. For a customer support agent routing tickets, 90% completion with clean escalation paths is acceptable. For an agent making financial decisions, you may need 99% before taking it anywhere near production. The key question is not just the percentage but what happens to the tasks that do not complete. A clean escalation to a human counts as a success, not a failure.
How do I test an AI agent for consistency?
Run the same input through the agent at least 20 times under the same conditions and compare outputs. A well-designed agent should produce consistent results on the same task. If outputs vary significantly, the problem is usually in the prompt structure, the temperature setting, or an over-reliance on non-deterministic reasoning steps. Consistency testing has caught more pre-production issues for us than accuracy benchmarks.
What is an explainability trace and why do regulated industries require it?
An explainability trace is a structured record of what information the agent used, what options it considered, and why it made the decision it did. For regulated industries like finance or healthcare, being able to audit a specific decision after the fact is often a legal requirement. For all industries, it is a debugging necessity. Without it, investigating why an agent behaved unexpectedly becomes far harder.
