AI · Framework
AutoAgent Optimization Journey Chart
Step chart tracking benchmark gains across autonomous harness-tuning experiments
This diagram documents the iterative progression of AutoAgent across 32 autonomous experiments on SpreadsheetBench and TerminalBench. It maps which specific interventions, prompt updates, and tool additions produced performance jumps versus which changes were discarded. It serves as a practical reference for designing autonomous agent self-improvement loops and debugging benchmarks.
The framework
Spreadsheet path: baseline ~39% → structured reasoning workflow (+ex...) → ~83% → compute... → write scripts to files + verify fu... → verify with data_only=... → strengthen formula-vs-value + mand... → ~88% → retry with exponential backoff for... → bonus verification phase → reasoning effort high → parallel to... → truncation=auto → pre-scan file listing + 50K trunc... → NO_OUTPUT → actionable error hints in Shell to... → shell time... → re-run of 0.962 code → ~97%. Terminal path: baseline ~27% → detailed prompt + reasoning high → prompt_cache_retention=24h → truncation=auto → bonus verification phase (15 tus...) → added read_file + write_file tools → ~55% plateau.
What it's for
- Design iterative optimization loops for autonomous AI agent harnesses
- Identify high-impact harness interventions like prompt caching, verification phases, and error handling
- Analyze common plateau points when benchmarking LLMs on terminal and spreadsheet tasks
Key points
- Tracks performance progression from baseline (~39% to ~97% on SpreadsheetBench, ~27% to ~55% on TerminalBench)
- Highlights successful tweaks including file verification, high reasoning effort, and retry backoffs
- Maps system-level improvements like truncation controls, file listing pre-scans, and actionable shell error hints
- Visualizes discarded experiments faintly against retained improvements
Pricing
Free
Details
- Type
- Framework
- Category
- AI
- Topic
- AI Evaluation
- Open source
- Yes
- Platforms
- other
- For
- AI researchers, benchmark evaluators, and LLM agent developers
- Tags
- agents, benchmarking, prompt engineering, optimization, evaluation, spreadsheetbench
Why it was saved
- @kevingu: Visualizes the autonomous harness-tuning loop and which interventions produced gains.
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Updated