How to Generate Technical Documentation with AI (Save Hours Per Sprint)

Arise · 2026-03-17 · 7 min read

The Documentation Tax Nobody Talks About

Every developer knows the drill: you ship a feature on Friday, the PM asks "is the documentation updated?" on Monday, and suddenly you're spending three hours writing markdown instead of building.

Studies show developers spend 15-20% of their time on documentation. For a team of five, that's an entire engineer's worth of output — gone to README files and API references nobody reads until something breaks.

The worst part? Docs go stale the moment you push new code. What if AI could generate accurate, up-to-date documentation directly from your source code, comments, and type definitions?

What AI Documentation Agents Do

• Parse source code to extract function signatures, types, and docstrings
• Generate README files with install instructions, usage examples, and API tables
• Create API reference docs from route handlers and schema definitions
• Write onboarding guides that explain project architecture to new team members
• Update existing docs when code changes, keeping everything in sync

Installation

First, install the AgentPlace CLI:

curl -fsSL https://www.agentplace.sh/install.sh | sh

Then install the Research Agent (doubles as a documentation generator when pointed at codebases):

agentplace install research-agent

Basic Usage — Generate a README

Point the agent at your project directory and get a complete README:

agentplace run research-agent --topic "Generate a comprehensive README.md for the project at ./my-app" --depth detailed

This scans your project structure, package.json (or equivalent), source files, and existing docs to produce a structured README with:

• Project description and badges
• Installation steps
• Usage examples
• Configuration options
• Contributing guidelines

Generate API Documentation

For REST APIs, the agent can parse your route handlers and produce reference docs:

agentplace run research-agent --topic "Document all API endpoints in ./src/routes/ with request/response examples" --format markdown

Sample output structure:

## POST /api/users

Creates a new user account.

**Request Body:**
| Field    | Type   | Required | Description          |
|----------|--------|----------|----------------------|
| email    | string | Yes      | User email address   |
| name     | string | Yes      | Display name         |
| role     | string | No       | Default: "member"    |

**Response (201):**
| Field     | Type   | Description            |
|-----------|--------|------------------------|
| id        | string | Unique user ID         |
| email     | string | Confirmed email        |
| createdAt | string | ISO 8601 timestamp     |

Generate Onboarding Docs for New Engineers

New hires spend their first week reading code and asking "what does this service do?" The agent creates architecture overviews:

agentplace run research-agent --topic "Write a developer onboarding guide for ./my-app covering architecture, key modules, data flow, and local dev setup" --depth comprehensive

This produces a guide covering:

• Architecture overview — what each service/module does
• Data flow — how requests move through the system
• Key files — where to find the important stuff
• Local development — how to run, test, and debug
• Common tasks — "how do I add a new endpoint?" type walkthroughs

Advanced Configuration

Customize output with a config file for consistent doc generation across your team:

{
  "agent": "research-agent",
  "task": "documentation",
  "options": {
    "source_dir": "./src",
    "output_dir": "./docs",
    "format": "markdown",
    "include_examples": true,
    "include_types": true,
    "sections": ["overview", "installation", "api-reference", "configuration", "contributing"],
    "style": "concise"
  }
}

agentplace run research-agent --config ./docs-config.json

AI Docs vs Manual Docs vs Doc Generators

CI/CD Integration — Auto-Update Docs on Push

Add doc generation to your pipeline so docs never go stale:

# .github/workflows/update-docs.yml excerpt
- name: Regenerate API docs
  run: |
    agentplace run research-agent \
      --topic "Update API docs in ./docs/api.md based on current ./src/routes/" \
      --depth detailed
    git diff --quiet docs/ || (git add docs/ && git commit -m "docs: auto-update API reference" && git push)

Tips and Best Practices

• Start with README generation — it is the highest-impact, lowest-effort win
• Run doc generation weekly via CI, not just on-demand — stale docs are worse than no docs
• Review AI output before publishing — AI gets the structure right but might miss business context
• Combine with type definitions — TypeScript interfaces and JSON schemas give the agent better signal
• Version your doc configs — treat documentation rules like code so the whole team stays consistent

What It Cannot Do (Yet)

AI documentation agents work best with well-structured code. If your project has zero comments, no types, and spaghetti architecture, the output will reflect that. Think of it as a 10x amplifier — it makes good code documentation great, but it will not fix bad code organization for you.

Conclusion

Documentation does not have to be the task everyone dreads. AI agents turn your codebase into readable, structured docs in minutes — from READMEs to API references to onboarding guides. Stop burning sprint hours on markdown and let the agent handle it.

Get the Research Agent on AgentPlace