Airbyte Expands Agentic Data Platform with Semantic Search and Fine-Grained Governance

Airbyte, an open data movement platform provider, has enhanced the Airbyte Agents platform to help artificial intelligence agents to discover relevant enterprise knowledge while ensuring organizations maintain precise control over what agents and users can access.

Airbyte's semantic search support and new entity policies for workspaces provide meaning-based retrieval and fine-grained governance built directly into the Context Store, a replicated, search-optimized index that is part of the Airbyte Agents platform.

"AI agents are only as valuable as the context they can safely access," said Michel Tricot, CEO and co-founder of Airbyte, in a statement. "Organizations don't need another disconnected vector database or another permission system, they need agents that understand the information that already exists across their business while respecting the same governance policies employees rely on every day. These new capabilities move us another step closer to making enterprise AI both more useful and more trustworthy."

Airbyte Agents now supports semantic search across content stored in Google Drive, Gong call transcripts, Granola meeting notes, and Linear issues and comments. This enables AI agents to retrieve information based on meaning rather than exact keywords. Instead of relying on literal text matches, semantic search understands intent and context. An agent can identify customer conversations discussing pricing concerns, contract objections, competitive products, login failures, or engineering issues, even when those exact terms never appear.

Last month, Airbyte Agents added a workspaces feature that gives users their own separate instance for Airbyte Agents. Each workspace can have its own users and defined access to specific data connectors. Now, a new Airbyte Agents feature introduces entity policies for workspaces, which provide precise control over which data connectors, data sources, and other resources, can be discovered and accessed by users and AI agents.

Organizations can now define policies that determine visibility and access at the entity level (data connector, data sources), allowing teams to expose only the information appropriate for specific departments, projects, or users. This enables agents to be deployed at scale with security controls. Now, read and write policies can be assigned to every user and agent in every data connector, across every workspace. This enables data connectors to be shared among users and agents while keeping sensitive information restricted to those with appropriate permissions.

This enables companies to do the following:

• Restrict agent access to data sources;
• Separate development, staging, and production environments;
• Limit visibility of sensitive business systems; and
• Align AI access with existing organizational security and compliance requirements.