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Endigest AI Core Summary
This post describes how a GitHub Copilot Applied Science researcher built eval-agents, a tool to automate analysis of coding agent trajectories across benchmark runs.
•eval-agents was created to process hundreds of thousands of trajectory JSON lines from benchmarks like TerminalBench2 and SWEBench-Pro.
•The stack uses Copilot CLI with Claude Opus 4.6 in VSCode, leveraging the Copilot SDK for built-in tools and MCP servers.
•Prompting strategy: be verbose and conversational, use /plan mode before /autopilot for complex tasks.
•Architectural strategy: prioritize refactoring, documentation, and tests to keep the codebase agent-navigable.
•Iteration strategy: shift from "trust but verify" to "blame process, not agents" — use strict typing, linters, and contract tests as guardrails.
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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