18 docs tagged with "ai"

Establish robust AI governance frameworks that maintain compliance, mitigate risks, and drive trust through visibility, lifecycle management, and policy enforcement

Configure AI asset access in personas: control who can view, edit, or manage AI models, model versions, applications, and governance properties.

Integrate, catalog, and govern Amazon SageMaker AI assets in Atlan.

Atlan AI is the intelligence layer of the Enterprise Context Layer. Context Agents enrich your data estate at scale; the Remote MCP server makes that context callable by every AI agent your organization runs.

Security and compliance information for Atlan AI, including AI architecture, data handling, encryption, model management, and compliance frameworks.

Atlan MCP is a hosted server that lets AI clients (Claude, Cursor, ChatGPT, Gemini, Copilot) and automation platforms (Python, n8n, LangChain) use Atlan as a context layer through the Model Context Protocol.

Reference for every tool available in the Atlan MCP server—search, lineage, metadata, governance, glossary, data quality, and more. Filter by category or access level.

Automate metadata enrichment at scale using AI-powered context agents that generate descriptions, READMEs, and SQL intelligence across your most important data assets.

Bootstrap, test, and ship the business context every AI agent needs to produce accurate, trustworthy answers.

Step-by-step guide to triggering AI-powered metadata enrichment using context agents in Context Agents Studio.

Answers to common questions about Context Agents Studio—covering enrichment behavior, collections, agent support, processing time, and AI credit usage.

Query Lakehouse metadata using natural language in AI coding agents like Claude Code. Install the atlan-lakehouse skill, which detects your platform and generates appropriate SQL queries.

Understand the collections available in Context Agents Studio—curated groups of data assets automatically surfaced from usage signals to help you prioritize metadata enrichment.

Learn about the AI-powered context agents available in Context Agents Studio—specialized agents that generate descriptions, READMEs, and SQL intelligence.

Before using Atlan AI for glossary management, your admin user must enable the Atlan AI Summary toggle in the Atlan AI section under Labs.

Learn what AI assets and metadata Atlan crawls from Amazon SageMaker.

Context Engineering Studio (CES) is Atlan's workspace for building, testing, and deploying the business context AI agents need to answer questions accurately. CES generates a context repository (a versioned bundle of skills, knowledge, and tools) from your data catalog, BI lineage, and semantic sources, then deploys it to any MCP-compatible agent, Snowflake Cortex, or Databricks Genie.

Learn how to write custom instructions that help Context Agents Studio generate accurate, business-relevant metadata descriptions for your organization.