Greptile Summary

This PR adds 17 new agent evaluation tasks to the Stagehand evaluation suite as part of STG-653. The new evaluations test the AI agent's capabilities across diverse real-world scenarios including e-commerce (Amazon shoes, Google Shopping, UberEats), entertainment platforms (Steam Games, Apple TV), research tools (arXiv, Hugging Face, WolframAlpha), and various web services (GitHub, Google Maps, NBA trades on ESPN, hotel booking).

All new evaluation files follow the established pattern from the existing agent evaluation framework:

• Navigate to target website using stagehand.page.goto()
• Create an agent with dynamic provider selection based on model name (Claude uses "anthropic", others use "openai")
• Execute specific instructions with defined step limits (typically 14-30 steps)
• Evaluate success based on agentResult.success property
• Include proper error handling, logging, and resource cleanup

The tasks are added to evals.config.json under the 'agent' category, integrating them into the existing evaluation pipeline. These evaluations expand test coverage to validate agent performance across complex multi-step workflows like checkout processes, search filtering, information extraction, and form filling on production websites.

Confidence score: 3/5

• This PR requires careful review due to several evaluation quality issues and potential risks from using production websites
• Score lowered due to lack of proper result validation in most tasks, reliance on production sites that may change, and some logical flaws in evaluation criteria
• Pay close attention to evals/tasks/agent/kith.ts for payment form risks, evals/tasks/agent/hotel_booking.ts for validation gaps, and the formatting issue in evals.config.json

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Greptile Summary

This review covers only the changes made since the last review (commit e71810e), not the entire PR.

The most recent changes complete a major refactoring of the agent evaluation system by centralizing agent initialization logic. The key changes include:

1. Agent initialization centralization: All agent evaluation functions have been updated to receive a pre-configured agent parameter instead of creating their own agent instances. This eliminates the duplicate model selection and provider mapping logic that was scattered across individual evaluation files.

2. Type system updates: The StagehandInitResult type in types/evals.ts now includes an agent property using ReturnType<Stagehand["agent"]>, enabling evaluation functions to access agent functionality through dependency injection.

3. Centralized configuration: The initStagehand.ts file now includes Computer Use Agent (CUA) model detection logic that automatically determines if a model supports computer use capabilities (checking for 'computer-use-preview' or models starting with 'claude') and creates appropriate agent configurations with proper provider mapping.

4. Standardized evaluation pattern: All ~20 agent evaluation files now follow a consistent pattern where they receive a pre-initialized agent, execute instructions using agent.execute(), and validate results based on agentResult.success. This creates uniformity across the evaluation suite.

The refactoring moves from a decentralized approach where each evaluation file handled its own agent setup to a centralized dependency injection pattern. This architectural change reduces code duplication, ensures consistent agent configuration across all evaluations, and provides better maintainability for global agent behavior modifications. The changes integrate with the existing evaluation framework by extending the StagehandInitResult interface and updating the initialization flow to provide agent functionality to evaluation tasks.

Confidence score: 4/5

• This PR is safe to merge with good architectural improvements and consistent patterns
• Score reflects clean refactoring with proper type safety, though some evaluations lack robust result validation
• Pay close attention to files with time-dependent instructions or weak validation logic

_{29 files reviewed, 4 comments}

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