AdaMAST: Boosting AI Agent Performance with Failure Taxonomies
abeirami · x · 2026-07-31
Developer @mertcemri introduced AdaMAST, a method that learns system-specific failure taxonomies from raw execution traces. It reuses these insights across subsequent runs to identify exactly where, when, and how AI agents fail.
In testing, integrating it as a Claude Code skill boosted the SWE-bench Verified Mini pass rate from 64.0% to 70.7%. On TerminalBench 2, using AdaMAST-generated taxonomies with an Opus 4.6/Forgecode harness and a Best-of-N Judge achieved a high score of 89.9%.
Related event: AdaMAST Automates Agent Failure Classification(2 posts)→
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