If it had a lossless, massive context window (100m-1b tokens), then it will squash everything. Give it bash + r/w and it can in theory /goal anything.
I think there's something to be gained in a production environment be siloing agents for reproducebility/auditability, but I suspect that will go away in the future.
There's that video of a silly demo someone made of an OS that was just nested copilot instances that generated the HTML of each window, which allowed you to do whatever you could imagine. It was seen as silly because it was, but that seems truly transformative.
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 a 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.
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.
embedding-shape•45m ago
> 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.