The development industry is shifting toward a standard of managing AI agents via files in repositories, such as AGENTS.md and CLAUDE.md. However, textual instructions quickly become outdated and mislead agents. The Hunch project has introduced version v1.1, which addresses this problem by linking Markdown text to an architectural decision graph.
What Happened
The Hunch team released update v1.1, implementing a mechanism to synchronize textual documentation with the actual state of the codebase. Using special HTML comments in Markdown files, it is now possible to "bind" descriptions to specific architectural decisions in a decision graph. This allows for the use of the hunch drift command within CI/CD processes to automatically block changes if new code edits contradict documented rules.
Context
Modern AI agents rely on instruction files in the repository root to understand project context. However, without verification mechanisms, these files turn into passive descriptions that fail to keep pace with dynamic code changes, creating a "documentation drift" effect where the agent receives incorrect or outdated information about the system structure.
Why It Matters for the Industry
This technology transforms documentation from a passive set of instructions into an active quality assurance (QA) tool. It creates a new niche in ensuring development consistency and allows for the integration of "prose" (instruction) verification directly into the Software Development Life Cycle (SDLC) and CI/CD pipelines, preventing architectural chaos at early stages.
Why It Matters for Users
For developers and users of AI assistants, this means a reduced risk of agent hallucinations when working with a codebase. The tooling guarantees that an assistant will not suggest erroneous or outdated architectural patterns simply because they are still described in a README or AGENTS.md.
Sources
Author
Look at AI, Editorial Team