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DocLayer
Engineering contract layer & safety harness for AI agents
DocLayer is a zero-dependency engineering contract layer and safety harness for AI coding agents. When autonomous agents encounter errors, code alone doesn't convey operational prohibitions. DocLayer introduces: • doclayer explain: Inspect declared contracts and negative invariants before writing code. • doclayer check: Machine-check AST contract drift in CI. • 4-tier epistemic model: Separate facts from decisions. • Self-verifying: Governs its own Python codebase with zero dependencies.
Hey Product Hunt community! 👋
I'm Parag, creator of DocLayer (https://github.com/paragpallavsi...).
I built DocLayer after noticing a critical gap with autonomous AI coding agents:
Agents don't just read code; they take real actions. But source code alone cannot convey what NOT to do.
When an agent hits an error like SQLite lock contention (`ERR_DB_LOCKED`), a naive agent might just delete the `.db` file to clear the lock - causing catastrophic data loss.
DocLayer introduces a durable engineering contract layer:
- `doclayer explain `: AI agents run this before coding to see declared contracts, active safety invariants, and negative prohibitions (e.g. "NEVER delete database on lock; apply backoff retry").
- `doclayer check --strict`: CI linter that catches AST contract drift when code changes.
It is 100% open-source, written in Python with zero external runtime dependencies, and uses its own engine to govern its own codebase!
Would love to hear your feedback, thoughts on agent safety, and feature suggestions! 🚀
About DocLayer on Product Hunt
“Engineering contract layer & safety harness for AI agents”
DocLayer was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #42 on the daily leaderboard. DocLayer is a zero-dependency engineering contract layer and safety harness for AI coding agents. When autonomous agents encounter errors, code alone doesn't convey operational prohibitions. DocLayer introduces: • doclayer explain: Inspect declared contracts and negative invariants before writing code. • doclayer check: Machine-check AST contract drift in CI. • 4-tier epistemic model: Separate facts from decisions. • Self-verifying: Governs its own Python codebase with zero dependencies.
On the analytics side, DocLayer competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how DocLayer performed against the three products that launched closest to it on the same day.
Who hunted DocLayer?
DocLayer was hunted by Parag Pallav Singh. 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.
For a complete overview of DocLayer including community comment highlights and product details, visit the product overview.
Parag Pallav Singh