> ## Documentation Index
> Fetch the complete documentation index at: https://wtd2026.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Writing: AI agents generating documentation

> Data on how people use agents to generate and maintain documentation.

**How AI agents generate and maintain documentation.**

The writing layer has three distinct session types, each with different trigger patterns, human involvement, and output quality. 73,058 requests across 3,710 sessions on a single Tuesday in April 2026.

## Scale

| Metric               | Value  |
| -------------------- | ------ |
| Total requests       | 73,058 |
| Unique sessions      | 3,710  |
| Requests per session | 19.7   |

The 19.7 requests per session reflects the agentic nature of writing: read existing docs, check the codebase, draft, verify, revise.

## By product

Writing requests break down across four products with different levels of human involvement.

| Product        | Requests | Share | Trigger               | Human input           |
| -------------- | -------- | ----- | --------------------- | --------------------- |
| Workflows      | 35,581   | 49%   | System (webhook/cron) | None                  |
| Atlas          | 34,177   | 47%   | Human (enters URL)    | None after initiation |
| Agent sessions | 2,200    | 3%    | Human (Linear issue)  | Continuous            |
| Agent sessions | 1,100    | 2%    | Human (Slack command) | Continuous            |

## Agentic reading versus writing

| Metric                    | Reading      | Writing     |
| ------------------------- | ------------ | ----------- |
| Requests per session      | 3.3          | 19.7        |
| Average prompt length     | 47 words     | 556 words   |
| Average response time     | 3.7 seconds  | 8.1 seconds |
| Median response time      | 2.9 seconds  | 4.1 seconds |
| Average prompt tokens     | Not included | 4,335       |
| Average completion tokens | Not included | 437         |

## Three session types

<Columns cols={3}>
  <Card title="Directed" icon="user">
    Human initiates on a Linear ticket or Slack command. Continuous human involvement. Includes edits, content creation, questions, PR work.
  </Card>

  <Card title="Automated" icon="zap">
    System-triggered by a webhook (push) or cron job. No human in the loop after setup. The majority of session volume.
  </Card>

  <Card title="Complex" icon="layers-2">
    Multi-agent pipelines with large system prompts. High coordination overhead. Atlas (repo-to-docs) is the primary example.
  </Card>
</Columns>

## Directed sessions

Humans trigger these via Linear ticket or Slack command and stay involved throughout.

### What people ask for

| Task type                | Share |
| ------------------------ | ----- |
| Edits                    | 27%   |
| Create new content       | 18%   |
| Questions                | 15%   |
| PR-related               | 12%   |
| Discussion               | 11%   |
| Review                   | 8%    |
| Information architecture | 5%    |
| Clarification            | 4%    |

### PR merge rate over time

AI-generated PRs from directed sessions have improved substantially:

| Month    | Merge rate |
| -------- | ---------- |
| January  | 47%        |
| February | 39%        |
| March    | 52%        |
| April    | 74%        |

### Prompt length

Directed session prompts average **1,133 characters**. Detailed enough to give an agent direction, short enough that humans write them.

## Automated sessions

System-triggered, no human required. The bulk of automated sessions run on a schedule or fire on a code push.

### Trigger type breakdown

| Trigger        | Share |
| -------------- | ----- |
| Cron jobs      | 70%   |
| Push-triggered | 30%   |

Push-triggered sessions produce 2.1 times more merged PRs on average. Likely because they're responding to code changes and a documentation need is immediate and specific.

### Prompt length

Automated session prompts average **1,146 characters**. Very similar to directed sessions.

## Complex sessions

Atlas (Mintlify's repo-to-docs generator) is a complex writing pipeline. A single Atlas run involves four distinct prompts.

| Prompt type   | Prompt size       |
| ------------- | ----------------- |
| Pre-analysis  | 7,000 characters  |
| Coordinator   | 19,000 characters |
| Writing agent | 5,000 characters  |
| Review agent  | 3,600 characters  |

The coordinator prompt alone (19,000 characters) is longer than most documentation pages. You can't just say "write good" to an agent.

## Benchmark

**Agentic PR merge rate: 55%+**

More than 55% of AI-generated PRs are merged.

## How to measure

The most universal proxy is **PR merge rate**. The share of AI-generated pull requests that a human accepts and merges. Other signals worth tracking:

* Time from generation to merge
* Review comments per PR
* Revert rate

Don't replace your deterministic tools. AI writing works alongside linters, validators, and style checkers.