AccInt launches a work-modeling system for AI agents that runs locally on user hardware, learns from real outcomes, and stores a complete audit trail of all decisions and results.

What happened: AccInt has released a system that allows AI agents to build and refine a persistent model of work processes on the user's own computer. The system learns by observing real outcomes—not assumptions—through pedagogical reinforcement learning, and records every step in an append-only ledger. A live operator shows 2,778 entities held in the work model, with 537 runtimes, 20,667 recorded events, and 1,931 outcomes validated by reality; the last 50 outcomes achieved a 94% success rate.

Why it matters: Conventional AI tools at work leave no record of decisions or lessons learned, making it hard for managers to understand what happened or why. AccInt addresses this by making the work model itself a persistent company asset—audited, scored by results, and fully owned on your hardware rather than in the cloud. This means both accountability (management can see the decision trail) and continuous improvement (verified work steps replay near-instantly on future runs, reducing cost and reasoning time).

What to watch: The system runs as a single small program and SQLite file on any recent computer, with no cloud connection and no API key exposure. Early access invites are being issued in order. The product is currently visible through coding-agent terminals, though the underlying work model is designed to support any agent that can observe, act, and report outcomes.