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An Agent is an LLM-powered assistant that reasons across your room’s data — its spaces, tables, and sources — to answer questions, run analysis, and generate insights. Every response is grounded in your verified enterprise data, with a transparent view of how the agent got there.
Agents combine retrieval over your embeddings with an LLM’s reasoning, then orchestrate multi-step actions — querying tables, executing code, generating visualizations — to deliver context-aware answers.

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How an agent works

  1. Understands your question in natural language.
  2. Retrieves relevant context from the room’s spaces and data.
  3. Reasons and acts — chaining steps like querying tables, running Python, or calling tools.
  4. Responds with a grounded answer, including citations and a visible thought process.

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What shapes an agent

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Conversations, explainability, and sharing

Each conversation is a thread you can revisit, rename, and share as a read-only link. Every answer exposes its reasoning chain and citations, and finished agents can be deployed as branded public apps or connected over the MCP protocol.

Agents (Feature Guide)

Configure models, chat, explainability, and sharing.

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Example questions

Spaces

The embeddings agents reason over.

Sharing & Collaboration

Share threads and artifacts.

Integrations & MCP

Connect agents to your applications.

Deployment

Publish agents as public apps.