Agent Evaluation

Agent evaluation is the systematic process of assessing an AI agent's performance across multiple dimensions — not just whether it produces correct outputs, but how reliably, efficiently, and safely it operates in real-world conditions. As agents take on more complex and consequential tasks, rigorous evaluation becomes critical.

Why agent evaluation is different

Evaluating a simple AI model is relatively straightforward: give it inputs, check the outputs against known correct answers. Evaluating an agent is harder because agents take multi-step actions, use tools, make decisions under uncertainty, and interact with dynamic environments. A single evaluation metric rarely captures the full picture.

Key evaluation dimensions

• Task completion: Does the agent actually accomplish what it was asked to do? This is the baseline metric but insufficient on its own.
• Accuracy: When the agent produces information or makes decisions, are they correct? This requires ground-truth data or expert review.
• Reliability: Does the agent perform consistently, or does it succeed sometimes and fail unpredictably on similar tasks?
• Efficiency: How many steps, tool calls, and tokens does the agent use? An agent that arrives at the right answer after fifty API calls may be correct but impractical.
• Safety: Does the agent stay within its guardrails? Does it handle edge cases gracefully? Does it escalate appropriately?
• User experience: Is the agent's behaviour transparent and predictable? Can users understand why it took specific actions?

Evaluation methods

• Benchmark suites: Standardised task sets that allow comparison across different agents and versions. Useful for tracking improvement over time.
• Human evaluation: Domain experts review agent outputs and actions for quality, appropriateness, and accuracy. Essential for subjective or complex tasks.
• A/B testing: Running two agent versions simultaneously and comparing performance on real tasks.
• Red-teaming: Deliberately trying to make the agent fail or misbehave to discover vulnerabilities.
• Production monitoring: Tracking metrics like success rate, error rate, escalation frequency, and user satisfaction in live deployments.

Building an evaluation framework

Start by defining clear success criteria for your specific use case. What does "good" look like for this agent doing this task? Then build evaluation sets that cover common cases, edge cases, and adversarial cases. Run evaluations regularly — agent performance can change when underlying models are updated.