Documentation-First Agent Development¶
Most agents today are built through trial-and-error. Prompts are patched together, tools are added reactively, and no clear documentation of agent behavior exists.
Dokugent flips that pattern.
It promotes documentation-first AI development—where agents are scaffolded with Markdown plans, behavior criteria, and signed certificates before they are executed.
What "Documentation-First" Means¶
Instead of: - Prompts scattered across scripts or JSON blobs - Tools declared only at runtime - No persistent record of success/failure rules
You get: - A structured plan.index.md of what the agent will do - criteria.md for how results should be evaluated - conventions/ to support LLM-specific patterns - A signed .cert.json with metadata, version, and trust scope
This enables repeatable, reviewable, and verifiable agent creation.
Dokugent Components That Enable This¶
| File | Purpose |
|---|---|
plan.index.md | Defines the agent’s reasoning steps and tool usage |
criteria.md | Documents success/failure expectations |
*.cert.json | Signed snapshot of all reviewed agent inputs |
conventions/*.md | Custom patterns per LLM (e.g., Claude, Codex) |
preview report.json | Compile-time snapshot of readiness and metadata |
Why It Matters¶
- Reproducibility – Rebuild agent behavior deterministically
- Security – Prevent unintended changes or behavior drift
- Compliance – Support audits and certification
- Collaboration – Let others inspect or extend the agent safely
Ideal For¶
- AI teams building internal copilots or tools
- Research groups running evaluations on LLM behavior
- Safety-conscious orgs deploying agents in regulated domains
With Dokugent, you're not just building agents. You're building documentation, trust, and integrity into every one.