We’re building an open stack that lets AI coding agents deliver work with the discipline senior engineers expect. Our latest write-up, “Intent Weaving for AI Coding Agents,” breaks down how we encode strategy, policy, and telemetry into machine-executable intent, plus an honest inventory of where current agents fail (reasoning, repo awareness, testing, etc.).
Highlights: - Mission compiler that turns business objectives into guardrail-rich plans for agents. - Knowledge graph + policy DSL so automation stays inside governance envelopes. - Pain-points matrix from real deployments; new benchmarks that punish regressions, not just pass unit tests. - Open-source pieces as we release them; Commander is already MIT-licensed.
We’d love feedback from folks shipping agentic workflows or wrestling with AI codegen drift. Where should we push harder? What failure modes have we missed?
Link to our manifesto: https://autohand.ai/manifesto
Thanks for reading, and be kind. Creating a new category means stretching before the skills are perfect.
Here is an example of a state space map rendered in 2D by PCA. It maps LLM research papers from 2025. It does not have policies attached to state positions yet, but can be used as a visual map.
The projection: https://i.imgur.com/a9ESiXs.png
The map itself: https://pastebin.com/pmGzFcPM
A cool thing both for intent weaving and state space policy approach is that they do not prescribe a sequence of steps, they are more like a GPS map allowing rerouting towards goal state at any moment. This is a more flexible description than a static procedure.
