AI coding assistants don’t have an intelligence problem. They have a runtime discipline problem. Here is how I’m enforcing it.

The exact moment I got fed up with agentic IDEs wasn't when they hallucinated a library. It was when an agent skipped writing a test, blindly modified four unrelated files to fix a single bug, broke my local build, and then spent 15 minutes in a recursive "fix-forward" doom loop trying to guess its way out.

LLMs have immense raw intelligence, but they lack the foundational engineering instincts humans developed over decades. They don't plan, they don't checkpoint known-good states, and they have zero scope containment.

Instead of trying to prompt-engineer my 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! ⭐

Link:  https://github.com/MeherBhaskar/agent-rigor