RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents

2026-07-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

RigorBench is introduced as the first benchmark designed to measure process discipline in autonomous AI coding agents, addressing a gap where existing benchmarks focus solely on outcome correctness. Developed by Meher Sai Preetam Madiraju and Meher Bhaskar Madiraju, RigorBench evaluates agents across five pillars: Planning Fidelity, Verification Coverage, Recovery Efficiency, Abstention Quality, and Atomic Transition Integrity, aggregating them into a composite RigorScore. The benchmark includes 30 tasks across five categories. Experimental results, evaluating leading harnesses against baseline coding assistants, demonstrate that structured process discipline improves process quality scores by an average of 41% and raises downstream outcome correctness by 17%. The full benchmark, scoring rubrics, and trajectory analysis tools are open-sourced.

Key takeaway

For AI Scientists and Machine Learning Engineers developing autonomous coding agents, you should prioritize integrating structured discipline frameworks into your agent architectures. The quantitative evidence from RigorBench shows that focusing on "how" agents code, through explicit planning, verification, and efficient recovery, not only improves process quality by 41% but also boosts outcome correctness by 17%. Consider using process discipline metrics as training signals to foster more reliable and robust agent behavior, moving beyond outcome-only optimization.

Key insights

Evaluating AI coding agents' engineering process discipline is as crucial as their code's correctness.

Principles

• Process discipline significantly improves both process quality and outcome quality.
• How software is built strongly predicts its long-term quality, maintainability, and cost.
• Disciplined processes reduce defect rates and improve predictability.

Method

RigorBench analyzes the full execution trajectory of an agent, capturing plans, edits, tests, errors, and commits, to compute process quality scores across five defined pillars.

In practice

• Incorporate process discipline metrics into reward models for agent training.
• Implement structured discipline frameworks to improve agent planning and abstention.

Topics

• AI Coding Agents
• Software Engineering Benchmarks
• Process Discipline
• LLM Evaluation
• Agent Architectures
• RigorBench

Code references

• features/copilot
• google-gemini/gemini-cli
• MeherBhaskar/agent-rigor
• anthropics/agent-protocol
• addyosmani/agent-skills

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.