Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 28·5 min read
Same Model, Different Harness: Different Coding-Agent Results
TL;DR
Changing the harness configuration of a coding agent significantly impacts its performance, even when the underlying model remains the same. In tests, a tighter context led to higher failure rates but also improved solution rates on specific benchmarks.
✦ Why It Matters
Evaluate your coding agents by experimenting with different harness configurations to optimize performance.
Key Takeaways
- Experiment with harness configurations to improve coding agent outcomes.
- Monitor fail-to-pass fractions to assess performance changes.
- Utilize tighter contexts for specific coding benchmarks.
- Evaluate multiple models under the same harness conditions.
How It Works
The study compares two harness configurations: one providing full chronological context and another that shortens older results as new context fills. This mechanical adjustment influences how the coding agent interprets and utilizes past information, affecting its problem-solving capabilities.