Enforcing "discipline" into AI coding assistants
We’ve all watched an AI agent completely derail. You give it a localized bug to fix. Instead of writing a test or planning the change, it blindly modifies four unrelated files, breaks your local environment, and then spends the next 15 minutes in a frantic, recursive "fix-forward" spiral trying to patch its own mistakes.
The problem isn't that LLMs lack intelligence. It's that they lack engineering hygiene. They don't naturally checkpoint known-good states, they have zero scope containment, and they completely lack the defensive programming instincts humans spent decades developing. They operate like hyper-caffeinated juniors guessing their way through a codebase.
Instead of trying to prompt-engineer our way around this behavior (which always eventually fails as the context window grows), we decided to treat this as an architectural constraints problem.
We built and open-sourced agent-rigor, a framework that explicitly hardcodes traditional SDLC mechanics directly into modular "Agent Skills" that the LLM is forced to execute.
The repository is fully open-source, and the skill primitives are designed to be decoupled so you can plug them into your own custom agent architectures or wrappers. If you're building in this space or just want to stop your local agents from going rogue, check out the repository below and drop a star if you like what we're building! ⭐