Back to the AI workflow board
Internal AI Tool
Documentation Ops
Docs repo
AI operating rules

An Agentic Doc Repo Makes Documentation Retrievable

Instead of living like normal docs in Notion or Confluence, the repo is designed so agents can actually retrieve what they need.

The Problem

Traditional documentation systems store information, but they do not naturally make it easy for agents to find and use the right piece at the right time.

What Was Built

An experimental documentation concept where backend content is vectorized and stored in a way that makes it much easier for an agent to retrieve relevant information than with a standard docs setup.

Where AI Sits in the Workflow

AI is the reason the repository is structured this way in the first place: the docs are being shaped for retrieval, not just human browsing. A person still decides what belongs in the docs and updates the repo when the operating model changes.

Tools Used

Docs repo
AI operating rules

The Result

Less time is spent hunting for the right document because the repository is built for agent retrieval from the start.

Key Insight

The future of documentation is not just better writing. It is better retrieval.

Want this built for your business?

Want a workflow like this in your business? Talk to Dovid.

Start the conversation

More Examples

Internal AI Tool

One Shared Skills Folder Made Every Agent Portable

Instead of letting every agent create its own skills directory, one shared folder and a symlink setup made skills portable across apps.

Internal AI Tool

A Vectorized Memory Layer Gives Agents Shared Context

Important work context can be written once, stored in memory, and read back by agents through an MCP server later.

Internal AI Tool

A Daily Agent Turns Conversations Into Memory

Every day, a scheduled agent reviews Codex conversations, identifies what work is happening, and turns that into usable memory automatically.