AI forgets things
Your AI handles small tasks fine. As your project grows, it drops details. 45 out of 52 requirements? You won't notice the 7 missing until production breaks.
Discovery and implementation happen in parallel. Your code moves through layered quality gates. When something is unclear, the system stops and asks.
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Your AI handles small tasks fine. As your project grows, it drops details. 45 out of 52 requirements? You won't notice the 7 missing until production breaks.
When requirements are unclear, AI fills in the blanks with assumptions. You get code that looks right but does the wrong thing. The bug hides behind confident syntax.
A logic bug gets patched in the architecture layer. The patch works today but creates coupling that breaks tomorrow. No system ensures fixes go to the right level.
You are the only quality gatekeeper. One person, reviewing AI-generated code you may not fully understand.
The spec gets classified and routed into library buckets. Each bucket implements in parallel. When a slice hits a gap, planning researches and integrates a solution.
The system routes code to where it belongs and edits it in place. No extraction, no rephrasing. Your requirements travel through the pipeline without losing information.
When tests fail, an investigator works in a CI sandbox to root cause the failure. It applies a fix, confirms tests pass, then submits a report to the planner. No manual debugging.
A coverage ledger maps every spec requirement to the code that implements it. Nothing gets marked complete until the ledger confirms full coverage.
When the system hits something underspecified, it researches the space, generates diverse constraint options, and asks you to choose. Your answer becomes a permanent rule for the rest of the build.
Parallel work happens in isolated git worktrees. A clean branch holds only verified code. Changes cross from dirty to clean only after passing all tests.
Every decision the system makes gets written to append-only logs with integrity chaining. The chain is tamper-evident. If a log entry gets altered after the fact, the hash chain breaks.
Internal agents never contact you directly. They emit signals when blocked. The Intent Agent collects these into a priority queue with full context about what is unclear and which part of the build is waiting. You see one focused question at a time.
You provide a constraint, not a solution. The system decides how to implement it. Your constraint gets stored as YAML and applied to every future decision on that slice. The blocked work resumes. Everything else kept building the whole time.
Append-only logs record every signal and constraint the system processes. Pick any function in the output and trace the chain back to the spec line that produced it. The coverage ledger shows what is done and what is still missing.
Other tools give you raw AI output and leave quality control to you. Oulipoly Automaton runs code through layered gates before it reaches your branch. Every function that lands in your project has passed compliance checks at every layer. A coverage ledger maps each requirement to the code that implements it.
Internal benchmark: spec coverage across accuracy, architecture, and code quality
$0
$15/mo
TBD
No. Those give you one AI that writes code for you to review. Oulipoly Automaton runs your code through a pipeline of quality gates before it reaches your branch. The difference is who does the quality control: you alone, or a system of layered gates with demotion for failures.
No. You write specs in plain language. The system handles implementation and review. When it needs clarity, it asks you for constraints instead of guessing.
You don't review code directly. The system surfaces questions through an Intent Agent queue when it hits ambiguity. You answer with constraints. The system handles everything else autonomously through layered quality gates.
The workflow engine integrates with Claude Code, Cursor, and Windsurf. It adds a quality pipeline on top rather than replacing your existing tools.
The free local version never sends code anywhere except to the AI APIs you configure. Your code stays on your machine.
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Your AI writes the code. Oulipoly Automaton checks if it's right.