It’s an agentic assistant that unifies everything your organization knows — across chat, wikis, docs, tickets, drives, and systems of record — and answers questions in natural language with grounded, cited, permission-aware responses. Unlike a search box, it understands intent, reasons across sources, and can act on the answer.

Traditional search returns a list of links for you to open and read; a knowledge agent returns the answer, grounded in your content and backed by citations. It also retrieves agentically — decomposing the question, searching in parallel, reranking, and synthesizing — rather than doing a single keyword lookup.

It uses retrieval-augmented generation: it first retrieves your actual internal content, then generates an answer grounded in that context, with inline citations so anyone can verify the source. Answers are tied to evidence rather than the model’s guesses.

No. Retrieval is permission-aware — it honors the access controls of every source system and surfaces only content each person is already authorized to see. A contractor asking about compensation bands, for example, gets nothing they shouldn’t.

Chat and email (Slack, Teams, Gmail, Outlook), wikis and docs (Confluence, SharePoint, Notion), file stores (Google Drive, OneDrive), and systems of record (Jira, ServiceNow, Salesforce, and more) — via connectors that preserve each source’s permissions.

The most common reason is data quality: point an AI at a messy, contradictory knowledge estate and it will answer confidently and wrongly. That’s why governance matters — source-quality controls, stale- and duplicate-detection, relevancy tuning, and a feedback loop — so retrieval stays accurate as the estate grows.