What should an AI coding agent learn after a failed run?

A developer is building AccInt, a local Work Model for agent-run work that aims to determine what an AI coding agent should learn after a failed run. The project focuses on capturing settled commitments from agent activity, such as diffs, tests, and outcomes, to create a learning substrate that runs on user-controlled hardware. AccInt targets Claude Code, Codex, OpenCode, and MCP-style workflows near real repositories.

I am building AccInt https://accint.xyz/ https://accint.xyz/ , a local Work Model for agent-run work. The product is early, but the technical question is broader than one tool: When an AI coding agent fails, what exactly should be learned? Most agent-memory discussions stop at storing more context. That helps recall, but it does not answer the harder engineering question: which context, action, check, or decision actually helped a future run land? The unit I am testing is a settled commitment: For coding agents, this can be grounded in practical signals: That is the gap I am trying to make concrete with AccInt: not just a memory store, not just a trace viewer, and not just orchestration. A local learning substrate that turns agent activity into a Work Model, running on hardware you control. The first wedge is Claude Code / Codex / OpenCode / MCP-style workflows near real repos, because those runs already produce commitments, diffs, tests, and outcomes. If you use coding agents seriously, I would value feedback: Early access / context: https://accint.xyz/ https://accint.xyz/