I am building AccInt (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/)