Agent Evaluation

Accuracy — whether the agent gave the right answer — is necessary but insufficient. An agent that resolves tickets correctly but takes twelve conversational turns to do so is often worse than a human who resolves in two. Evaluation frameworks track multiple dimensions: task completion rate, average turns to resolution, escalation frequency, user satisfaction signals, and cost per interaction.

Equally important are failure mode metrics. How does the agent behave when it encounters a question outside its training? Does it gracefully escalate, confabulate a plausible-sounding wrong answer, or loop indefinitely? The distribution of failure types reveals more about production readiness than the success rate does. An agent with an 85% success rate and clean escalation on the other 15% is more deployable than one with 92% success and catastrophic failures on the remaining 8%.

Efficiency metrics capture whether the agent is actually saving time or merely shifting work. If the agent resolves 80% of tickets but the remaining 20% require more human effort to clean up than if a human had handled them from the start, the net productivity gain may be negative. Track both direct resolution metrics and the downstream effort created by agent interactions.

Cost metrics matter increasingly as AI agent usage scales. Cost per resolution, cost per conversation turn, and cost per token consumed provide visibility into whether the agent becomes more or less expensive as it handles more complex cases. Many teams discover that their agent's average cost is acceptable but the tail — the most complex 5% of cases — is disproportionately expensive.

Offline evaluation tests the agent against a curated dataset of inputs with known correct outcomes. This is useful for regression testing — ensuring that changes to the agent's prompts, tools, or model version don't degrade performance on established benchmarks. You can run offline evaluations quickly, cheaply, and repeatedly, making them ideal for the development loop.

The limitation is that curated datasets rarely capture the full diversity of production inputs. Real users ask questions in unexpected ways, combine multiple requests in a single message, provide incomplete information, change their mind mid-conversation, and use slang, abbreviations, and cultural references that don't appear in clean test data. Offline benchmarks measure performance on sanitised inputs, which can differ substantially from production performance.

Online evaluation monitors the agent's behaviour in production with real users. Metrics like resolution rate, escalation triggers, user re-contact frequency, and time-to-resolution provide ground truth that no offline benchmark can replicate. The trade-off is that online evaluation exposes real users to potential failures and requires longer observation periods to collect statistically significant data.

Most teams combine both: offline evaluation gates deployment (a change must pass the benchmark suite before reaching production), and online evaluation validates performance in the wild (monitoring dashboards track whether production metrics hold steady after deployment). This two-stage approach balances speed of development with production quality assurance.

When evaluation is built into the development loop, it shifts agent development from intuition-driven iteration to evidence-driven improvement. Instead of tweaking a prompt because it 'feels better,' teams run the modified agent against a test suite and compare metrics. This makes prompt engineering reproducible — changes that help on one category of inputs but hurt on another become visible immediately.

Evaluation frameworks also clarify what 'good enough' means for deployment decisions. Without metrics, teams endlessly polish agent behaviour on edge cases that rarely occur in production. With metrics, they can prioritise improvements by impact: fixing the failure mode that affects five percent of interactions before polishing the response style that affects a fraction of a percent.

The framework also enables A/B testing of agent changes. Rather than replacing the current agent wholesale, teams can route a percentage of traffic to the modified version and compare metrics head-to-head. This approach catches regressions that offline benchmarks miss and builds confidence before full rollout.

Evaluating on easy cases only is the most frequent mistake. Teams build test suites from examples where the agent already works well, then report high scores that collapse in production. Effective evaluation deliberately includes adversarial inputs, ambiguous queries, multi-step tasks, and edge cases that the agent is expected to handle poorly. The test suite should reflect the full distribution of production difficulty, not just the cases the developer chose to test.

Another pitfall is using a single aggregate metric. A 90% success rate hides whether failures are random or systematic. If the agent fails on every query involving date calculations or multi-product orders, that cluster of failures may affect a disproportionately valuable segment of users. Evaluation frameworks should segment performance by query type, complexity level, and user cohort to reveal these patterns.

Over-reliance on automated metrics is also risky. Some aspects of agent quality — tone appropriateness, empathy in difficult conversations, creative problem-solving — resist quantification. Periodic human review of agent transcripts remains essential even when automated metrics are comprehensive. A small sample of manually reviewed conversations per week often catches quality issues that metrics miss.

Start with real production data, not synthetic examples. Pull a representative sample of past interactions, including the difficult cases that caused escalations or complaints, and annotate them with expected outcomes. Synthetic test cases are useful for covering specific edge cases, but a test suite built entirely from synthetic data will not represent the actual distribution of inputs the agent faces in production.

Maintain the dataset as a living artefact. As new failure modes are discovered in production, add them to the test suite. As the product evolves and new use cases emerge, add representative queries for those use cases. A test suite that was comprehensive at launch becomes incomplete within months if it is not actively maintained. Assign ownership of the evaluation dataset to someone who reviews production failures regularly.

Stratify the dataset by difficulty and category. A test suite with 90% easy questions and 10% hard ones will produce misleadingly high scores. Weighting difficult cases appropriately — or reporting metrics separately for each difficulty tier — gives a more honest picture of agent capability and makes improvement efforts more targeted.