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.
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?
spec-written-into-the-repo is the right model - a persistent plan that survives context resets and that any agent can pick up is fundamentally different from a prompt you're manually re-feeding each session. the acceptance criteria + validation gates combo is the piece most agent frameworks don't bother with. curious how it handles cases where the atomic tasks turn out to be wrong mid-run - can you edit and resume without blowing the state?
The "repo as harness" idea is clever — giving agents durable context instead of a fresh chat window every time is exactly what long-horizon tasks need. Context drift is probably the #1 reason agent work falls apart mid-task.
Is the plan file something you generate once and manually update, or does it evolve automatically as the codebase changes?
Writing the plan into the repo rather than the context window is the right architecture. Durable state that survives model swaps and context resets is what makes long multi-step tasks actually viable. The validation gate pattern catches drift before it compounds. How are the gates implemented? Are they executable assertions the agent runs itself, or do they require human sign-off?
How do you keep the plan from getting stale as humans change the codebase between runs?
About Deep Work Plan on Product Hunt
“Models matter. Context matters more. Give your agent a plan.”
Deep Work Plan launched on Product Hunt on June 17th, 2026 and earned 101 upvotes and 9 comments, placing #9 on the daily leaderboard. 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.
Deep Work Plan was featured in Open Source (68.5k followers), Developer Tools (514.2k followers), Artificial Intelligence (471.2k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 208.6k products, making this a competitive space to launch in.
Who hunted Deep Work Plan?
Deep Work Plan was hunted by Sergio Florez. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Sergio Florez