Think of a typical loop we may ask of Claude Code today (assume we are not using TDD): run some test suite with fail fast mode, diagnose if the failure is due to recent feature changes (pass reference to backend/frontend, github issues, PRD,...). Ask CC to decide if test failed due to feature change and then update the test. Perhaps ask CC to use sub-agent to investigate and fix (if deemed so). Commit each fix, move on to next.

I know, this has so many ways to make blunder but I am talking about the agent here, not our error-prone test maintenance. What if we had an agent that had context of your codebase, deterministically ran test suite, linter, hooks, etc. The "English" prompt would become a code loop with the LLM only brought in to decide if a test has failed because of feature change. Also, we can extract git log, JIRA and what not.

Each tool here is real code. Executable code that calls others and only prompts when they meet edge cases. Edge cases are defined but we can now accelerate the maintenance of these tools using agents themselves. But the system is built on "programs that do one thing and do it well" and then reach out to an LLM for its specific edge case. The agent is how these executables work with each other.

> When in doubt, simplify. Remove, trim and minimize. Reproduce issues in as small cases as possible, understand the full design completely, there is no shortcuts for this.

My harness is a Claude Code plugin with its own brainstorming, adr, and planning skills with associated review and interview skills. Behavioral testing related to acceptance criteria is built in. Everything in my harness is gated to prevent ratholes.

I recently inflated a docker container to execute a set of work with Claude in unsafe mode and immediately saw problems with everything it was doing…and then I realized I had not installed my harness.

Running Claude without an engineering harness is like driving a car without brakes or a steering wheel.

I build precision-editing tools for AI coding agents (hic-ai.com) and worked out thousands of JSON-wrangling and regex issues, so I can verify they are indeed a bit of a pain, across all possible failure modes that AI coding agents and models and harnesses can produce. Anyway, I completely agreed with everything in your article, though I would suggest however that agents need *three* things at runtime to fix a defect: great logging and a clear error response (just like you have it), but also, precision-editing tools that enable agents to make the minimal, surgical change without touching or copying any other portion of the file. These actually change not just the feedback but also the options available to the agent and capabilities in the midst of the workflow to self-heal. If Ambiance adds a kernel to buffer the LLM from the outside world, HIC Mouse adds a "kernel" or buffer between the LLM and its own environment and file system. Anyway, this is such a cool project. Please reach out if you ever add MCP support for Ambiance -- I'm happy to release a new version of Mouse that supports it. Again, great work.