How to evaluate web and browser agents

Web and browser agent evaluation assesses whether an AI system completes browser-based tasks through acceptable actions and verifiable end states, accounting for the changing page representations—such as screenshots, DOM snapshots, and accessibility trees—that agents use to select targets. Unlike standard LLM evaluations that primarily grade final responses, these evaluations examine the full interaction trajectory, including element grounding, action types and values, page-state transitions, task completion, extraction accuracy, efficiency, loops, timeouts, latency, and cost. Live websites introduce variability from layout changes, A/B tests, and altered markup, making reproducible testing environments and separate diagnosis of agent regressions versus website drift important. Effective traces capture the agent’s observations, actions, execution results, and resulting browser states at every step, typically organized into parent-child spans for multi-step tasks. Scoring can combine code-based checks for structured actions and outcomes with calibrated LLM-based judgment for trajectory interpretation, while regression datasets should be built from production failures, reset between trials, and include independently verifiable success criteria. Braintrust is presented as a platform for tracing browser runs, attaching page evidence, promoting failed production traces into datasets, comparing experiments against baselines, and applying evaluation gates to model, prompt, or agent changes.

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