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Understand context agents

Context agents are specialized AI agents in Context Agents Studio, each designed to handle a specific type of metadata enrichment. Currently available agents are Description, README, and SQL Intelligence. Link Terms is coming soon. Each agent focuses on one output and applies it across all un-enriched assets in a collection in a single action.

What makes context agents different from generic AI generation is that they don't work from asset names alone. Each agent analyzes the full context available in Atlan—table structure, query history, lineage relationships, and the organization's business glossary—so the output reflects how your organization actually uses the data, not just what the schema says.

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Agents only enrich assets that are missing the target metadata attribute. Existing values are never overwritten.

Description agent​

The Description agent generates semantic, business-meaningful descriptions for data assets. Rather than producing generic restatements of column names, it synthesizes the full context around each asset to produce descriptions that reflect how the data is actually used.

To generate each description, the agent:

  • Reads the table and column names from the asset's metadata
  • Traces upstream and downstream dependencies in the lineage graph
  • Analyzes query patterns from your data warehouse's query history
  • Matches asset context with your business glossary terminology
  • Writes the generated description back to the asset

README agent​

The README agent generates higher-level documentation for assets, going beyond individual attribute descriptions to cover the broader purpose, usage patterns, and context of a data asset. READMEs are designed for someone arriving at an asset for the first time who wants to understand what it's and whether it's relevant to their needs.

Where the Description agent focuses on what an asset is, the README agent focuses on how it fits into the broader data ecosystem: its position in the pipeline, how it's commonly used, and what downstream reports or analysis it powers.

To generate each README, the agent:

  • Reads the asset's structure and properties from its metadata
  • Maps the asset's position in the data pipeline using the lineage graph
  • Identifies common usage patterns from query history
  • Pulls in related documentation linked to the asset
  • Writes the generated README back to the asset

SQL Intelligence agent​

The SQL Intelligence agent surfaces valuable patterns from your organization's query history and attaches them to the relevant data assets. Instead of requiring analysts to manually document how tables are used, this agent derives it from actual usage: which tables are most commonly joined together, which filter conditions appear most frequently, what business questions the data is regularly asked to answer, and what foreign key relationships exist.

These insights are registered as separate assets in Atlan and linked to the relevant tables, making them reusable across discovery, data quality, and other use cases beyond enrichment.

To generate insights, the agent:

  • Analyzes query logs for recurring patterns across your query history
  • Identifies tables that are commonly joined together using the lineage graph
  • Extracts frequent filter conditions from query activity
  • Surfaces recurring business questions derived from query patterns
  • Discovers foreign key relationships between tables
  • Attaches the generated insights as linked assets on the relevant tables
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SQL Intelligence requires tables and views to have associated query history from connected data warehouse sources.

Coming soon​

The Link Terms agent identifies and links relevant business glossary terms to data assets, establishing semantic connections between business concepts and technical assets. It bridges the gap between how business users talk about data and how it's technically structured in the warehouse.

The agent uses semantic similarity—not keyword matching—to identify which glossary terms are meaningfully related to each asset. Matches are ranked and filtered before being applied to maintain link quality.

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