Evaluating AI agents is fundamentally more complex than evaluating language models. A language model is evaluated on individual outputs, but an agent must be evaluated on entire trajectories of decisions and actions that unfold over multiple steps. A correct final answer reached through a wasteful path of unnecessary tool calls is a different outcome than the same answer reached efficiently. A task completed successfully but with unintended side effects is not a simple success. Agent evaluation must capture these nuances to guide meaningful improvement.

Core Evaluation Dimensions

Task Completion Rate

The most fundamental metric is whether the agent accomplished the task. Task completion rate measures the percentage of tasks the agent successfully completes. This requires clear, verifiable definitions of what "complete" means for each task type. For a coding agent, completion might mean all tests pass. For a research agent, completion might mean producing a synthesis that covers all required topics.

Efficiency

Efficiency measures the resources consumed to complete a task: number of LLM calls, number of tool invocations, total tokens processed, and wall-clock time. An agent that completes a task in 5 steps is more efficient than one that completes the same task in 50 steps, even if both succeed. Efficiency directly impacts cost and user experience.

Reliability

Reliability measures consistency across multiple runs. An agent that succeeds 95% of the time is more reliable than one that succeeds 60% of the time, even if the 60% agent occasionally produces better results. For production systems, reliability is often more important than peak performance.

A reliable agent that completes 80% of tasks consistently is more valuable in production than a brilliant agent that completes 95% of tasks but fails unpredictably on the other 5%. Predictability matters as much as capability.

Quality Metrics

Output Quality

Beyond binary success or failure, the quality of the agent's output matters. A coding agent might produce code that passes tests but is poorly structured and unmaintainable. A writing agent might complete a document that is factually correct but badly organized. Quality metrics are task-specific and often require human evaluation or specialized automated assessments.

Side Effect Assessment

Agents can complete their primary task while causing unintended consequences. A coding agent might fix a bug by introducing a workaround that creates technical debt. A data analysis agent might produce correct results but leave temporary files cluttering the file system. Side effect assessment evaluates whether the agent's actions were clean and confined to the intended scope.

Key Takeaway

Agent evaluation must go beyond "did it work?" to include "how well did it work?", "how efficiently did it work?", and "did it cause any unintended harm?" Multi-dimensional evaluation prevents optimizing for one metric at the expense of others.

Established Benchmarks

SWE-bench

SWE-bench evaluates coding agents on real GitHub issues from popular open-source projects. Each task requires the agent to understand the issue, navigate the codebase, implement a fix, and pass the project's test suite. This benchmark has become the standard for comparing coding agent capabilities, with scores steadily improving from single digits to over 50% as agent architectures mature.

WebArena and VisualWebArena

These benchmarks evaluate agents that interact with web applications. Tasks include booking flights, shopping online, and managing accounts, requiring the agent to navigate complex UIs, fill forms, and complete multi-step workflows. They test both the agent's reasoning ability and its skill at interacting with real web interfaces.

GAIA

GAIA (General AI Assistants) evaluates agents on practical tasks that require multiple tools and reasoning steps. Tasks range from simple (requiring one or two tools) to complex (requiring many tools, multi-hop reasoning, and synthesis). GAIA provides a graduated assessment of agent capability across difficulty levels.

Building Custom Evaluation Suites

Production agents need evaluation suites tailored to their specific use case. Building an effective custom evaluation requires:

  1. Representative tasks: Collect real tasks from production usage, ensuring coverage of common cases, edge cases, and failure-prone scenarios
  2. Ground truth definitions: For each task, define what a correct completion looks like, what a partial success looks like, and what constitutes failure
  3. Automated verification: Where possible, create automated checks for task completion, such as test suites, output validators, or comparison against expected results
  4. Human evaluation protocol: For tasks that cannot be automatically verified, define a structured evaluation rubric for human judges
  5. Regression testing: Maintain a set of previously failed tasks to verify that improvements do not reintroduce solved problems

Evaluation Methodology

Trajectory Analysis

Beyond evaluating final outcomes, trajectory analysis examines the agent's decision-making process. Did the agent take a direct path to the solution, or did it wander? Did it recover effectively from errors, or did it get stuck in loops? Did it use appropriate tools at appropriate times? Trajectory analysis reveals systematic weaknesses that outcome-based metrics might miss.

Ablation Studies

Ablation studies systematically remove or modify components of the agent to understand their contribution. Removing the re-ranking step, disabling a specific tool, or changing the planning strategy and measuring the impact on performance reveals which components are most critical.

The most informative evaluation is not the one that tells you your agent's success rate. It is the one that tells you why your agent fails and what to fix first. Design evaluations to diagnose, not just measure.

Monitoring in Production

Lab evaluations do not fully predict production performance. Production monitoring tracks real-world agent behavior through user satisfaction scores, task completion rates on real requests, error rates and common failure modes, resource consumption and cost trends, and escalation and fallback rates.

These production metrics should feed back into your evaluation suite. Tasks that fail in production become test cases for your offline evaluation, creating a continuous improvement loop.

Key Takeaway

Agent evaluation is a continuous practice, not a one-time assessment. Build evaluation into your development workflow with automated benchmarks for every change, periodic human evaluations, and continuous production monitoring. The teams that evaluate most rigorously improve fastest.

As agents take on more complex and consequential tasks, evaluation practices will need to mature accordingly. The field is moving toward standardized evaluation frameworks, shared benchmarks, and better tools for trajectory analysis. Investing in robust evaluation infrastructure now will pay dividends as your agent systems grow in capability and responsibility.