Human + AI Collaboration Model
This knowledge base is meant to be agent-native: built and maintained by humans and AI agents working together, not just written by humans and occasionally summarized by AI.
Division of labor
Humans contribute:
- Interviews
- Expert knowledge
- Observations
- Strategic decisions
- Hypotheses
Agents contribute:
- Research summaries
- Document synthesis
- Classification and tagging
- Cross-linking
- Contradiction detection
- Question generation
- First drafts
Workflow
Interview recording
↓
Transcript
↓
AI extraction agent
↓
Structured Markdown document
↓
Human validation
↓
Published knowledge
The AI extraction step turns a raw transcript into a first-draft document following the interview template. A human always validates before anything is treated as "published knowledge" — agents draft, humans decide.
Planned first research agents
None of these are built yet; this is the initial roster from the roadmap.
| Agent | Role |
|---|---|
| Market researcher | Surfaces and summarizes external research, reports, and organizations relevant to market analysis and ecosystem/organizations |
| Interview synthesizer | Turns raw transcripts into structured drafts using the interview template |
| Knowledge curator | Maintains cross-links, tags, and structure across the knowledge base; flags orphaned or stale pages |
| Contradiction detector | Compares new claims against the hypotheses log and existing documents, flags conflicts instead of silently resolving them |
Why this matters beyond the docs site
The meta-objective is that this knowledge system is itself an early prototype of the eventual product: an AI-assisted operational intelligence system that turns fragmented knowledge into coordinated action. See knowledge graph vision for where this is headed structurally.