Building an AI agent is the easy part. Knowing whether it's actually working? That's where most teams struggle.
We've deployed agents across lead qualification, document processing, and customer support. Every deployment taught us the same lesson: you can't improve what you don't measure, and most teams measure the wrong things.
Why Traditional Software Testing Fails for Agents
Agents aren't deterministic. The same input can produce different outputs depending on model temperature, tool responses, and conversation history. Unit tests help, but they don't catch the failures that matter in production:
The agent used the right tool but asked the wrong question
The output was technically correct but unhelpful
The agent escalated too aggressively (or not enough)
Performance degraded gradually over weeks without anyone noticing
You need a different evaluation framework, one built for non-deterministic, multi-step systems.
The Four Pillars of Agent Evaluation
1. Task Success Rate
The most important metric: did the agent complete the task correctly? This sounds simple but requires clear task definitions:
Define "success" precisely. For a lead qualification agent, success might mean: correctly scored the lead AND routed to the right SDR AND drafted a relevant outreach message.
Track partial success. An agent that correctly identifies a lead as qualified but writes a bad email is different from one that misqualifies entirely.
Measure against human baseline. What's the human success rate on the same task? Your agent doesn't need to be perfect. It needs to be better (or faster, or cheaper) than the alternative.
2. Tool Use Accuracy
Agents break most often at tool boundaries: calling the wrong API, passing bad parameters, or misinterpreting responses. Track:
Tool selection accuracy: Did the agent pick the right tool for the situation?
Parameter correctness: Were the inputs to the tool valid and complete?
Response handling: Did the agent correctly interpret the tool's output?
Error recovery: When a tool call failed, did the agent retry sensibly or crash?
3. Latency Budget
Users don't care about your architecture. They care about speed. Set latency budgets for each step:
End-to-end: Total time from input to final output
Per-step: Time spent on each node in the graph (helps identify bottlenecks)
LLM calls: How many model calls does the agent make? Each one adds latency and cost
Tool calls: External API latency is often the biggest bottleneck
Set alerts for P95 latency, not just averages. The worst 5% of experiences define your reputation.
4. Human Review Loop
Even the best agents need human oversight. Build a structured review process:
Sample review: Review a random sample of agent outputs daily (5-10% is usually enough)
Failure analysis: When the agent fails, categorize WHY. Wrong tool? Bad reasoning? Missing context? Each category gets a different fix.
Edge case capture: Collect inputs that the agent handled poorly and add them to your test suite
Feedback loop: Insights from human review should feed back into prompt improvements and tool updates weekly
Setting Up Your Evaluation Pipeline
Here's the evaluation stack we use for our client deployments:
Tracing: Every agent run is traced end-to-end. Every LLM call, tool call, and decision point is logged with inputs and outputs
Automated scoring: A separate LLM evaluates the agent's output against predefined rubrics (correctness, completeness, tone)
Dashboard: Real-time metrics on success rate, latency, cost per task, and escalation rate
Alerts: Automatic alerts when success rate drops below threshold or latency spikes
Weekly review: Human review of sampled outputs + failure analysis + prompt iteration
The goal isn't zero failures. It's fast detection and fast improvement. An agent that fails 5% of the time but improves every week will outperform one that's 95% accurate but never gets better.
Common Mistakes We See
Only measuring happy paths. Your agent works great on clean inputs. But what about typos, ambiguous requests, and edge cases?
No baseline comparison. "The agent got 80% right" means nothing without context. 80% vs. a human's 70%? Great. 80% vs. 95%? Problem.
Evaluating too late. Don't wait until production to evaluate. Run evals on every prompt change, every tool update, every model version.
Ignoring cost. An agent that's 99% accurate but costs $5 per task might be worse than one that's 92% accurate at $0.10 per task.
Start Simple, Iterate Fast
You don't need a perfect evaluation system on day one. Start with task success rate and latency. Add tool accuracy tracking once you've identified your bottlenecks. Build the human review loop once you have enough volume to sample meaningfully.
The teams that win aren't the ones with the best initial agent. They're the ones with the tightest feedback loop.