Technical writing workflows are changing: AI coding agents like Devin, Claude Code, and Cursor can now draft documentation, suggest edits, and help maintain consistency across large repositories.
Unlike simpler AI writing tools, these agents can navigate codebases, follow project-specific style guides, and generate content that's aware of your existing docs. They're particularly useful for first drafts, repetitive updates, and catching inconsistencies, though they still need human direction for strategy, accuracy, and understanding what readers actually need.
TLDR:
- AI agents like Devin, Claude Code, and Cursor assist with technical writing tasks from drafting to PRs.
- These agents work in different environments—IDE, terminal, or async—so teams often use multiple.
- Repository-level config files (
.cursorrules,CLAUDE.md) enforce style consistency across agents. - Fern Writer operates as a Slack-based agent purpose-built for documentation using Devin's architecture.
Why AI agents matter for technical writing
Technical writing bridges the gap between complex engineering systems and the developers who need to use them. The goal isn't just creating documentation—it's reducing friction so developers can solve problems independently. This includes API references, integration guides, and tutorials that must balance clarity with technical precision.
This work has traditionally been manual and time-intensive. AI coding agents offer a meaningful shift: unlike autocomplete tools limited to single files, agents can navigate your codebase, understand project context, and handle multi-step tasks like drafting docs, checking consistency, and opening pull requests. They're well-suited to the repetitive, structured parts of documentation work—freeing up time for the higher-judgment work that still needs a human.
To be effective in documentation workflows, these agents need specific capabilities beyond general-purpose coding assistance.
Evaluating agents for technical writing: Five key considerations
Getting value from agents in documentation workflows requires more than just picking a tool. The following considerations will help you set up agents effectively, regardless of which ones you use.
Repository configuration and workflow integration
Agents work best when your documentation standards are codified in the repository itself—not in wikis or onboarding docs that agents can't access.
Configuration files like CLAUDE.md and .cursorrules can define style guidelines, but they're also where you specify file naming conventions, directory structures, frontmatter requirements, and which files agents should or shouldn't touch. Some teams include build commands so agents can validate output before committing, or define PR templates to keep agent contributions consistent with human ones.
CI integration extends this further. Linting, link checking, and build validation on commit mean agent output goes through the same quality gates as human contributions—so review focuses on content rather than catching mechanical errors.
The broader principle: the more your standards live in code rather than process documentation, the more useful agents become.
Review workflows and human-in-the-loop patterns
Agent output needs review, but how you review matters. Treating agent-generated docs like human-written PRs misses failure modes that are specific to agents.
Agents are generally reliable for mechanical tasks: updating parameter names, applying terminology changes across files, fixing formatting inconsistencies. They're less reliable for judgment calls, like deciding what information a developer actually needs, knowing when an example is helpful versus confusing, or understanding which details matter for a specific audience. Review effort should match this. Humans reviewing AI-generated documentation should skim the mechanical changes, read the conceptual explanations closely.
Reviewing in chunks helps for larger updates. Rather than waiting for an agent to finish a full task, review sections as they're completed. Early feedback prevents the agent from propagating a misunderstanding across an entire doc set.
The most common failure mode is missing context, not wrong syntax. An agent might correctly document what an API parameter does while missing why a developer would use it. Or it updates a reference accurately but doesn't notice that the tutorial on the next page now contradicts it. Review should check cross-references, examples, and narrative coherence—the connective tissue that agents tend to miss.
When you see recurring issues, update your configuration files. If an agent keeps using passive voice, add that to your style rules. If it puts examples in the wrong format, specify the format explicitly. If it misses cross-references, add a checklist it should run through before committing. The goal is turning review feedback into configuration changes so you're not catching the same issues repeatedly.
Framework awareness and tooling integration
Generic models often produce Markdown that looks valid but breaks when processed by your actual build pipeline—especially for documentation systems using custom directives, shortcodes, or React components.
Include framework-specific guidance in your agent configuration files. Specify which components exist, show correct syntax examples, and note common mistakes to avoid. If your framework has unusual frontmatter requirements or nonstandard Markdown extensions, document those explicitly—agents won't infer them from context.
When possible, have agents validate their output before committing. A build check or linter that catches MDX syntax errors means review focuses on whether the content is accurate, not whether it compiles.
Integration surface and workflow placement
Where an agent runs affects how quickly you can iterate on documentation. Cursor embeds in the editor, letting you draft docs alongside the code you're documenting. Claude Code targets terminal-based workflows, fitting teams that manage docs through build scripts and CLI tooling. Devin operates in a remote sandbox, handling documentation updates asynchronously via pull requests.
Many teams use multiple tools rather than picking one. An engineer might use Cursor to update docs alongside a code change, while a dedicated documentation contributor uses Claude Code for batch updates across the repository. Devin can handle routine documentation maintenance in the background while humans focus on higher-judgment work.
There's also value in documentation contributors using the same tools as the engineering team. If engineers are already working in Cursor with established .cursorrules, anyone writing docs inherits that configuration automatically. Shared tooling means shared context—agents working on documentation have access to the same codebase understanding as agents working on code.
Autonomy levels
Different documentation tasks call for different levels of agent independence.
Low-autonomy mode works best for content requiring judgment: conceptual explanations, getting-started guides, anything where tone and framing matter. Use inline suggestions and treat the agent as a drafting partner. Higher autonomy suits mechanical tasks: updating a parameter name across twenty files, applying terminology changes, reformatting code examples to match a new style guide.
Start with lower autonomy than you think you need. As you learn where an agent is reliable for your specific docs, give it more independence for those task types. The goal is matching oversight to risk, not minimizing your involvement across the board.
Fern Writer: specialized Slack agent for Fern docs
The agents discussed above are general-purpose coding tools adapted for documentation. For teams using Fern Docs, Fern Writer is purpose-built for the platform.
Most documentation feedback happens in Slack—"this endpoint description is outdated," "we renamed this field," "can we add an example here?" But acting on that feedback means context-switching to your repo, finding the right file, and making the edit. Fern Writer turns Slack messages into pull requests without leaving the conversation.
Tag @Fern Writer in any channel where it's installed and describe what you need. It analyzes your docs, determines what to update, and opens a PR. You can request changes by replying in Slack or commenting on the PR directly.
Beyond simple edits, teams use it to review docs for inconsistencies, implement style changes across pages, update docs based on GitHub issues, or prepare documentation for an upcoming code PR. Because it's built for Fern Docs, the agent understands Fern's component library and navigation structure, and you can customize its behavior via an AGENTS.md file in your repository.
Final thoughts on technical writing automation
The key to working with AI agents on documentation is treating your standards as code: define style guidelines in repository files, calibrate autonomy to the task, and build feedback loops that turn recurring review issues into better agent instructions.
For teams using Fern, Fern Writer extends this approach with native understanding of your documentation structure. But regardless of which tools you use, the goal is the same—spend less time on mechanical updates so you can focus on whether the documentation actually helps the developers reading it.
FAQ
How do AI agents differ from traditional autocomplete tools for technical writing?
Autocomplete tools suggest text within a single file based on immediate context. AI agents can navigate your codebase, follow project-specific configuration files, and handle multi-step tasks like drafting across multiple files or opening pull requests. The difference is scope: autocomplete helps you write faster, agents help you manage documentation tasks.
Which AI agent should I choose for real-time documentation drafting?
For real-time drafting, an IDE-embedded tool like Cursor works well—you get inline suggestions you can accept or reject as you write, with minimal context switching. Terminal-based tools like Claude Code suit workflows where you're already working in the command line. The best choice depends on where you already spend your time.
How can I prevent AI agents from generating incorrect syntax in my documentation?
Put your standards where agents can read them. Configuration files like CLAUDE.md or .cursorrules can specify style rules, component syntax, and formatting requirements. For framework-specific documentation, tools like Fern's llms.txt give agents a reference for valid syntax. When possible, have agents run a build check before committing so errors get caught early.
What review workflow should I use for high-stakes technical documentation?
For content where tone and accuracy matter—conceptual explanations, getting-started guides—use inline suggestions and review each change before accepting. Save higher-autonomy workflows for mechanical tasks like terminology updates or reformatting. Match oversight to risk.
Can AI agents handle docs-as-code workflows with version control?
Yes. Most agents integrate with Git, whether through terminal commands, IDE tooling, or automated PR generation. The more useful question is how well they integrate with your specific workflow—configuration files, CI checks, and PR templates all help agent contributions go through the same quality gates as human ones.
