AccInt: a Work Model for AI coding agents

A developer has built AccInt, a local work loop for AI coding agents that implements a Work Model to track context, decisions, failed attempts, and outcomes. The system uses late-interaction retrieval, scored tokens, commitments, and surprise-gated credit to reinforce useful context only when validated by real results.

I have been building AccInt https://accint.xyz/ , a local work loop for AI coding agents. The short version: agents do not just need generic memory. They need a Work Model : a record of the context retrieved, decisions made, failed attempts, tests run, and outcomes that proved whether the work actually landed. That matters because repeated agent work usually fails in the same places: AccInt is my attempt at making that feedback loop explicit. It uses late-interaction / MaxSim retrieval over scored tokens, commitments and outcomes, and surprise-gated credit so useful context gets stronger only when reality validates it. I am especially looking for feedback from people using Claude Code, OpenCode, Codex, or building agentic devtools / RAG systems: Early access: https://accint.xyz/ https://accint.xyz/