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The Knowledge feature powers your AI agents with accurate, up-to-date information. Import documentation from external platforms, create custom content, and let AI deliver precise answers to your users.
Knowledge Management showing documents with their sources, page IDs, and status

Knowledge Management - Browse Documentation

What is Knowledge?

Knowledge is AGO’s document management and retrieval system. It stores your organization’s documentation, processes it for AI understanding, and enables your agents to find and deliver relevant information in conversations.

Key Concepts

Knowledge Sources

A source is a connection to documentation. Sources can be:
  • External connectors: Notion, Confluence, Zendesk, etc.
  • Manual sources: Documents created directly in AGO
  • File imports: PDFs and other documents

Documents

Documents are individual pieces of content within a source. Each document has:
  • Title and content
  • Optional translations
  • Version history
  • Quality score
  • Related questions

Hierarchy

Documents can be organized hierarchically:

Embeddings

Documents are converted to vector embeddings for semantic search. This allows the AI to find relevant content even when users don’t use exact keywords.

Core Capabilities

19 Platform Connectors

Import from Zendesk, Notion, Confluence, Intercom, HelpScout, and more

Automatic Sync

Keep content fresh with minute, daily, or weekly sync schedules

Multi-Language

Serve global users with document translations in 12+ languages

Quality Analysis

AI-powered scoring (0-5 scale) with actionable improvement suggestions

AI Ingestion

Extract knowledge from PDFs, Word, and text with automatic deduplication

Version History

Track changes with automatic versioning and full content snapshots

Change Proposals

Review, accept, or reject proposed content changes before applying

Update Requests

Track knowledge gaps identified from conversations

Document Attachments

Upload images and files, reference them in Markdown content

How AI Uses Knowledge

When a user asks a question:
  1. Query Processing: User’s question is converted to a vector
  2. Semantic Search: Most relevant documents are retrieved
  3. Context Assembly: Top documents form the AI’s context
  4. Response Generation: AI answers using the retrieved knowledge
  5. Source Attribution: Response can include document references

Architecture Overview

Knowledge Connectors

Set up your first connector

Source Management

Add and manage sources

Knowledge Quality

Analyze and improve content

Change Proposals

Manage content changes

Update Requests

Track knowledge gaps

Best Practices

Content strategy

Setup Tutorial

Step-by-step tutorial

API Reference

Programmatic access