atlan

A practical workflow

1. Unify business systems into an Enterprise Data Graph using connectors.
2. Use AI agents to bootstrap the context layer by generating descriptions and linking terms.
3. Domain experts collaborate to resolve conflicts, annotate, and certify the context layer.

Based on the sources below. Editorial review does not imply hands-on product testing.

Candidates based on category and primary feature. Check feature and pricing differences before switching.

What changed

Changes to the facts recorded here, not a live scan of every vendor update. Save this tool to follow updates in your account.

1. 9/13/2026

   Updated: best for, features, limitations, summary, use cases, walkthrough

   See recorded changes

   bestFor

   Before: ["Enterprises deploying AI agents that require integrated business context to improve AI reasoning and usefulness."]

   After: ["Enterprises deploying AI agents that need to understand business context to move from prototype to production.","AI-forward enterprises seeking to articulate their own knowledge, data, and processes to build useful AI."]

   features

   Before: ["Unifies business systems into an Enterprise Data Graph with 80+ connectors.","AI agents generate asset descriptions, link business terms, and surface top business questions to bootstrap the context layer.","Human experts resolve conflicts, annotate, and certify the AI-generated context before it is finalized."]

   After: ["Unifies business systems into an Enterprise Data Graph with 80+ connectors.","AI agents bootstrap the context layer by reading the Enterprise Data Graph and generating asset descriptions, linking business terms, and surfacing top business questions.","Collaborative human review resolves conflicts, annotates edge cases, and certifies production-ready context."]

   limitations

   Before: ["AI agents alone cannot reason effectively without a translation layer that interprets human language against data structure."]

   After: ["AI-generated context drafts require human review and certification before production use."]

   summary

   Before: "Building a shared understanding of data, business logic, and institutional knowledge for AI tools. Unifies business systems into an Enterprise Data Graph with 80+ connectors. AI agents generate asset descriptions, link business terms, and surface top business questions to bootstrap the context layer."

   After: "Atlan provides an enterprise AI context layer that integrates data, business logic, and governance to enable AI agents to act on trusted data. It unifies business systems into an Enterprise Data Graph and uses AI to bootstrap and collaborate on building a context layer for AI applications."

   useCases

   Before: ["Building a shared understanding of data, business logic, and institutional knowledge for AI tools.","Scaling context development across data estates to improve AI agent effectiveness in enterprises."]

   After: ["Building a shared understanding of data, business logic, and institutional knowledge for AI tools.","Improving AI agent reasoning by providing knowledge, expertise, and norms context."]

   walkthrough

   Before: []

   After: ["Unify business systems into an Enterprise Data Graph using connectors.","Use AI agents to bootstrap the context layer by generating descriptions and linking terms.","Domain experts collaborate to resolve conflicts, annotate, and certify the context layer."]