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Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?

Paper: 2602.11988

Authors: Thibaud Gloaguen, Niels Mündler, Mark Müller, Veselin Raychev, Martin Vechev

Published: February 2026

Summary

This paper evaluates whether repository-level context files (like ) actually help coding agents perform better on real-world software engineering tasks.

Key Findings

Performance Impact

  • LLM-generated context files: Decrease task success rates by ~3% on average
  • Developer-provided context files: Marginally improve performance by ~4% on average
  • No context files: Baseline performance

Cost Impact

  • Context files increase inference costs by over 20% on average
  • More steps required to complete tasks (2.45-3.92 additional steps)

Behavioral Changes

  • More testing and exploration: Agents run more tests, search more files, read more files
  • Instruction following: Agents generally follow instructions in context files
  • Redundant documentation: Context files are often redundant with existing documentation
  • No effective overviews: Context files don't provide useful repository overviews

AGENTBENCH

The authors created a new benchmark called AGENTBENCH consisting of:

  • 138 unique instances from 12 repositories
  • Real GitHub issues (bug-fixing and feature addition tasks)
  • Developer-written context files
  • Python software engineering tasks

AGENTBENCH complements SWE-BENCH LITE (which uses popular repositories without context files).

Experimental Setup

Coding Agents Evaluated

  • CLAUDE CODE with SONNET-4.5
  • CODEX with GPT-5.2 and GPT-5.1 MINI
  • QWEN CODE with QWEN3-30B-CODER

Datasets

  • SWE-BENCH LITE: 300 tasks from 11 popular Python repositories (no context files)
  • AGENTBENCH: 138 tasks from 12 repositories with developer-provided context files

Settings Evaluated

  1. NONE: No context files
  2. LLM: LLM-generated context files (using agent-developer recommendations)
  3. HUM: Developer-provided context files

Key Insights

1. Context Files Make Tasks Harder

Instructions in context files increase reasoning tokens by 14-22%, suggesting tasks become more complex.

2. Context Files Are Redundant

When documentation files are removed from repositories, LLM-generated context files actually improve performance by 2.7% on average.

3. Stronger Models Don't Generate Better Context Files

Using GPT-5.2 to generate context files improves SWE-BENCH LITE performance by 2% but degrades AGENTBENCH performance by 3%.

4. Context Files Encourage Exploration

Agents use more repository-specific tools (e.g., , ) and run more tests when context files are present.

Recommendations

  1. Omit LLM-generated context files for now, contrary to agent-developer recommendations
  2. Include only minimal requirements in context files (e.g., specific tooling to use)
  3. Human-written context files should describe only essential information
  4. Future work: Improve automatic generation of concise, task-relevant guidance

Limitations

  • Evaluation focused heavily on Python (a language well-represented in training data)
  • Context files are a recent development (August 2025)
  • Popular repositories used in benchmarks may not be representative of most codebases
  • SWE-BENCH: Repository-level coding agent evaluation
  • AGENTBENCH: New benchmark for context file evaluation
  • Context files: AGENTS.md, CLAUDE.md, README files for agents

Conclusion

Context files have only a marginal effect on coding agent behavior. While they encourage broader exploration and instruction following, they don't provide effective repository overviews and often make tasks harder. The authors recommend omitting LLM-generated context files and including only minimal requirements in human-written ones.

Tags: #agents #context-files #evaluation #SWE-bench #LLM-agents

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