Deep Work Plan
Deep Work Plan is an open-source tool that embeds a structured plan directly into a code repository, ensuring AI agents do not drift from the intended task during long runs. It uses atomic tasks, acceptance criteria, and validation gates to keep agents on track, and supports resumable state across interruptions.
Deep Work Plan: Models matter. Context matters more. Give your agent a plan. | Product Hunt
Deep Work Plan
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Models matter. Context matters more. Give your agent a plan.
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Models matter. Context matters more. Give your agent a plan.
15 followers
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AI Coding Agents
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LLM Developer Tools
Deep Work Plan turns any repo into a harness with the context of your best engineer — so any AI agent codes like your smartest model and can't drift from the plan. Not a chat window it forgets, a spec written into the repo: atomic tasks, acceptance criteria, validation gates, resumable state. Long runs survive context resets; any agent picks up where the last left off. Point an agent at it, walk away, come back to work you can verify. Any agent, any repo, no lock-in. Open Source, MIT.
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Hi Product Hunt 👋
Models matter. Context matters more. That one line is the whole reason this exists.
I build with AI agents every day, and I kept hitting the same wall: an agent starts a long task brilliantly, then somewhere around hour three it quietly drifts. The diff still compiles — it's just not what I asked for. There was never a clean way to resume, because the whole plan lived in a chat window that had grown too long to trust.
I stopped treating that as a prompting problem and started treating it as a structural one. The fix wasn't a smarter model. It was giving the agent a plan it couldn't drift from — written into the repository itself.
That's Deep Work Plan. The idea is two moves:
1) Make the plan the source of truth, not the chat. Before any code, you write a spec: a goal, atomic tasks, and for each task explicit acceptance criteria + a validation gate. "Done" is decided by the gate, not by how the model feels. And it lives on disk, so it survives a context reset or a handoff to a different agent tomorrow.
2) Let the repository be the harness. The context (files), the tools (your scripts and tests), the guardrails (the plan and its gates), the state (on disk) — all of it lives in the repo as plain files any agent can read. So it's tool-agnostic: Claude Code, Codex, Cursor, or next year's agent can all run the same plan. No vendor to bet on.
What I'm proudest of is that it's not a slide. It's dogfooded across three repos — including the site that documents it. It's MIT, and you can install it into your own repo in one step at deepworkplan.com/init.
If your agents start strong and wander by hour three, I'd genuinely love your take. How are you keeping long-horizon agent work on track today?
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2d ago