How We Hit 83.4% on SWE-bench Verified (Part 2): Finding the Root Cause and Generating the Fix

We recently tested an AI debugging methodology on SWE-bench Verified and achieved a combined pass rate of 83.4% . Our overview post covers the full methodology, results, and high-level thinking — if you haven't read it yet, that's a good place to start. The methodology breaks down into three stages: reproduce the bug → generate a fix → verify the fix is trustworthy . This series walks through each stage and explains how runtime facts guide the AI toward the right answer at every step. Part 1 covered the Reproduce stage: before touching any code, the agent runs the program to collect real call chains and argument data — runtime facts — so it's working from evidence instead of guesswork. This post answers one question: once you have those runtime facts, how do you make sure the agent changes the right code? A lot of AI agents don't fail because they can't write a patch. They fail because they write the patch too early. The agent sees where the error is thrown, immediately adds a defensiv