Evaluating Agent Accuracy: Beyond Pass/Fail Testing

Why Standard Testing Is Insufficient

Unit tests and integration tests verify that an agent behaves correctly on known inputs. Production evaluation addresses a different question: how is the agent performing on the real distribution of inputs it encounters day-to-day — including the novel cases, the edge cases, and the cases that weren't in the training data?

Production evaluation requires a fundamentally different approach than pre-deployment testing. It requires ongoing data collection, ground truth labelling, statistical analysis of performance trends, and mechanisms to detect degradation before it becomes visible in business outcomes.

The Core Metrics Stack

• Task completion rate: percentage of tasks the agent completes without escalating
• Autonomous accuracy rate: percentage of completed tasks where the agent decision was correct
• Escalation rate: percentage of tasks routed to human review (leading indicator of degradation)
• False confidence rate: percentage of autonomously completed tasks where the agent was wrong but confident
• Outcome accuracy rate: percentage of agent decisions that produced the correct downstream outcome

Measuring Outcome Accuracy

Outcome accuracy is the hardest metric to collect — it requires knowing what the correct outcome was for each agent decision. For some decision types, ground truth is observable quickly: did the routed lead convert? Did the extracted invoice data match the payment that was eventually made? For others, ground truth requires manual labelling.

Build the ground truth collection mechanism before the agent goes live. For high-volume decision types, sample-based labelling (reviewing 5–10% of decisions on an ongoing basis) is sufficient to track accuracy trends. For high-consequence decision types, 100% review is warranted until accuracy is established.

Detecting Degradation Before It Becomes Visible

Agent performance degrades gradually. The first signal is typically an increase in escalation rate — the agent is becoming less confident — followed by an increase in override rate on escalated decisions — the agent is escalating cases it's handling incorrectly. If left unaddressed, these leading indicators eventually become visible in business outcomes.

Monitor escalation rate and override rate in real time, with automated alerts when either metric moves more than one standard deviation from baseline. Early detection gives the operations team time to investigate and retrain before degradation affects outcomes.