· 4 min read
Technical writing is one of the most natural applications for agent teams. The work is structured, the quality criteria are well-defined, and the output formats are predictable. A single agent writing documentation produces decent first drafts. Multiple agents — each handling a different aspect of the documentation process — produce drafts that are closer to publish-ready.
The key insight: documentation isn't one task. It's research, structuring, writing, example generation, and review. Splitting those into specialized agents produces better results at each stage.
API docs require a strict progression from analysis to writing to examples to review. A Sequential Pipeline with 4 agents handles this naturally.
Role: Parse API specifications and extract the structural information needed for documentation.
This agent takes your API spec (OpenAPI, GraphQL schema, or even raw endpoint lists) and produces a structured inventory: endpoints grouped by resource, request/response schemas, authentication requirements, rate limits, and error codes. It identifies which endpoints are CRUD operations, which are complex workflows, and which have notable edge cases.
Role: Transform the API analysis into clear, structured documentation.
Takes the Analyzer's output and writes the actual docs. Each endpoint gets a description, parameter table, request/response examples in a consistent format, and notes on common usage patterns. The writer follows your existing documentation style — formal or conversational, terse or detailed.
Role: Create realistic, copy-paste-ready code examples for every endpoint.
This agent produces code samples in your target languages (typically 2-3). Examples use realistic data, not "foo" and "bar." They show common use cases, error handling, and pagination patterns. Each example is self-contained — a developer should be able to copy it and run it with minimal modification.
Role: Review the complete documentation for accuracy, completeness, and consistency.
The reviewer checks that every endpoint is documented, examples match the documented parameters, error codes are complete, and terminology is consistent throughout. It produces a review report with specific issues to fix and an overall quality assessment.
The sequential structure matters here. Each agent builds on the previous agent's output. The Reviewer can't do its job without seeing the Writer's docs and the Generator's examples together.
User guides cover multiple independent topics. A Parallel Workers configuration with 4 agents handles different documentation areas simultaneously.
Role: Write detailed documentation for each product feature.
This agent produces feature-by-feature reference documentation. What the feature does, how to configure it, what options are available, and what the expected behavior is. Straightforward, comprehensive, and organized by feature area.
Role: Document end-to-end workflows that span multiple features.
Users don't think in features — they think in tasks. This agent writes step-by-step guides for common workflows: "How to set up your first project," "How to invite team members and manage permissions," "How to export data for reporting." Each workflow links back to the relevant feature documentation.
Role: Create a troubleshooting section covering common issues and their solutions.
This agent catalogs known issues, error messages, and common user mistakes. For each: the symptom, the cause, and the fix. Organized by category so users can quickly find their specific problem.
Role: Write the onboarding documentation that gets new users from zero to productive.
This is the most critical piece. The Getting-Started guide covers installation, initial setup, the first meaningful task, and next steps. It's opinionated — it doesn't show every option, just the fastest path to value.
These four agents work in parallel because their outputs are independent. The Workflow Mapper doesn't need to wait for the Feature Documenter to finish. A human editor assembles the final guide, stitching the sections together and ensuring cross-references are correct.
Release notes are a recurring documentation need. A lean 2-agent team keeps this efficient.
Role: Analyze the raw changelog (commits, PRs, tickets) and categorize changes.
This agent ingests your list of changes — whether from git logs, Jira tickets, or a manual list — and categorizes each one: new feature, improvement, bug fix, breaking change, deprecation, or internal change. It assesses user impact (high, medium, low) and identifies changes that need migration guidance.
Role: Transform the categorized changes into polished release notes.
Takes the Analyzer's structured output and writes release notes your users actually want to read. Features get descriptions that explain the benefit, not just the implementation. Bug fixes get concise one-liners. Breaking changes get prominent callouts with migration steps. The tone matches your product's voice.
Two agents are enough here because the task is well-scoped. Adding more would over-engineer it.
The biggest risk with multi-agent documentation is inconsistent language. Agent 1 calls it a "workspace," Agent 3 calls it a "project," and Agent 4 calls it an "environment."
Create a terminology guide as part of your input. List every key term and its canonical usage: "Always use 'workspace' (not 'project' or 'environment'). Always use 'team member' (not 'user' or 'collaborator')."
Include this terminology guide in every agent's prompt. It adds a few sentences of input and saves hours of find-and-replace in the output.
Lock your style decisions too. "Use second person ('you') not third person ('the user'). Use active voice. Code references in backticks. No exclamation marks." The more explicit your style rules, the more consistent your multi-agent output.