If you’ve spent any time working with autonomous AI coding agents recently, you know the drill.
You give the agent a straightforward task: "Add a user profile page and link it to the navbar."
The agent says, "I've got this." It writes some code. You run it, and it throws an import error. You paste the error back. The agent apologizes, rewrites the file, and now your routing is broken. You paste that error back. Ten iterations later, your config is mysteriously deleted, the navbar is entirely missing, and the agent is trying to install a deprecated version of React.
This is the AI Agent Doom Loop.
It happens because current agent frameworks mistake intelligence for discipline. We dump a 10,000-token SYSTEM_PROMPT.txt telling the agent everything about our project, hoping it remembers the architecture constraints on step 45 of its execution loop. It rarely does.
I built Agent Rigor because I got tired of babysitting agents that code themselves into corners.
The Root Cause: Context Rot
When an agent starts a task, its context is pristine. But as it reads files, executes commands, and hits errors, its context window fills up with junk stack traces and previous failed attempts.
By the time it's 20 steps deep, the original system prompt you carefully crafted is buried. The agent forgets the architecture guidelines. It starts prioritizing the immediate error in front of it over the overall goal. This is when it starts guessing, hallucinating, and making things worse.
The Solution: Progressive Disclosure and Empirical Discipline
Agent Rigor isn't a new LLM or a magic prompt wrapper. It's an operating system for agents that enforces strict empirical discipline.
Instead of one massive prompt, Agent Rigor uses a 3-tier hierarchy:
- L1 (Apex Kernel): The absolute, non-negotiable laws. (e.g., "Never guess an API signature. Always grep or read the file first.")
- L2 (Phase Directors): Orchestration that only loads when the agent enters a specific phase (Planning, Execution, Verification).
- L3 (Skill Protocols): Specific instructions for specific tasks (like self-correction), loaded only when that specific task is underway.
This is Progressive Disclosure. The agent only sees the instructions it absolutely needs for the current atomic step.
Furthermore, Agent Rigor enforces a "known-good state" policy. If a test fails, the agent doesn't just keep blindly modifying the same file. It reverts, analyzes the actual evidence, and plans a new approach. No more "this seems right" commits. Everything must be empirically proven before moving forward.
Stop Babysitting, Start Engineering
If you're building with Cursor, Copilot or really any coding assitant, you need a discipline layer. You can check out how we implemented it in the open-source repo here: Agent Rigor on GitHub.
If it saves you from reverting a mangled codebase at 2 AM, give it a star. More importantly, if you think the approach is wrong, open an issue and tell me why.