Objective
The agential semioverse repository can identify what needs improving, create plans to improve it, and execute those plans — with human oversight at the strategic level (setting goals, approving plans) but not at the tactical level (choosing files, writing frontmatter, running enrichment).
Key results
- Agent can compute a file quality score for any file in the repository and select the weakest file in a given scope
- Agent can apply at least 3 mechanical improvement operations (frontmatter enrichment, description inference, tag inference) without human guidance
- Agent can create a plan from an identified gap, gate-check it against the planning methodology, and propose it — without being told to
- Agent can determine its own next task by reading goals, plans, and constraints — not by waiting for instructions
- The ratio of inference-based to script-based operations decreases over time (progressive automation)
Serving plans
- 0015 (MCP tooling) — tool infrastructure
- 0016 (improve-files) — file improvement operations
- 0014 (stop cycling) — architectural discipline
- 0009 (proactive plan capture) — autonomous planning
Constraints
- No planning methodology exists to evaluate plans against (being addressed this session)
- Many improvement operations still require inference (not yet delegable to scripts or local models)
- No goal structure exists to connect plans to objectives (being addressed this session)