This is a useful reliability frame because self-repair is not enough unless the structure remains inspectable by a human.
In clinical AI, the equivalent would be an agent harness that declares role, sources, allowed tools, stop conditions, and the unresolved-work owner before it touches workflow state.
If the system can repair its own context but the team cannot understand what changed, repeatability has moved from the workflow into the agent's private memory.
That's a lot of file structuring just to provide syntactical metadata. It seems like it could become subjective and stale.
I've worked out a similar model to solve similar problems, but specifically leveraging deterministic script interrogation of a JSON file that contains the same concepts. The goal is programmatic trigger word (e.g. send email) search rather than “read a directory, infer what matters, read another directory, infer again,” ad nauseam.
The CLI scans all of the skills and retrieves only enough metadata to determine which skills are relevant. When the skill-to-keyword mappings are found to be wanting, AI can update them if a deeper scan identifies a heuristic gap, so that the next time the same concept appears, the correct skills are found deterministically.
That gives you progressive discovery without making repeated inference part of the normal retrieval path.
I wouldn't call it "syntactical metadata". I've found that allowing an agent to quickly know what exists and why it's important is critical in guiding agentic systems to act well, and thus, in my workflows, ensuring agents perform appropriately is a first order concern.
This is a useful reliability frame because self-repair is not enough unless the structure remains inspectable by a human.
In clinical AI, the equivalent would be an agent harness that declares role, sources, allowed tools, stop conditions, and the unresolved-work owner before it touches workflow state.
If the system can repair its own context but the team cannot understand what changed, repeatability has moved from the workflow into the agent's private memory.
That's a lot of file structuring just to provide syntactical metadata. It seems like it could become subjective and stale.
I've worked out a similar model to solve similar problems, but specifically leveraging deterministic script interrogation of a JSON file that contains the same concepts. The goal is programmatic trigger word (e.g. send email) search rather than “read a directory, infer what matters, read another directory, infer again,” ad nauseam.
The CLI scans all of the skills and retrieves only enough metadata to determine which skills are relevant. When the skill-to-keyword mappings are found to be wanting, AI can update them if a deeper scan identifies a heuristic gap, so that the next time the same concept appears, the correct skills are found deterministically.
That gives you progressive discovery without making repeated inference part of the normal retrieval path.
I wouldn't call it "syntactical metadata". I've found that allowing an agent to quickly know what exists and why it's important is critical in guiding agentic systems to act well, and thus, in my workflows, ensuring agents perform appropriately is a first order concern.