Airbyte Agents Launched to Fix the Data Problem Breaking AI Agents

Gives AI agents a unified view, replicated data ready to query – fixes fragmented, slow, and unreliable data access that causes agents to fail in production

Airbyte, creator of the open data movement platform, today launched Airbyte Agents, a context layer that gives production-grade agents direct access to a unified, search-optimized index of an organization’s data that is replicated and ready to query before the agents run.

Most agent failures in production are not model failures, they are data failures. Agents built on runtime API orchestration chain together five or six calls across disconnected systems to answer a single question, burning tokens, adding latency, and frequently returning stale or contradictory results. Airbyte Agents solves this at the data layer rather than the orchestration layer.

At the core of Airbyte Agents is the Context Store, a replicated, search-optimized index that unifies a company's data across systems before the agent ever runs. For example, customer records from Salesforce, tickets from Zendesk, issues from Jira, and conversations from Slack are brought together into a single queryable index with history and state preserved. The work of assembling context happens in advance, not at query time, so agents query the Context Store instead of chasing live APIs. That typically collapses five or six calls to one or two and dramatically reduces token consumption.

"Airbyte Agents has massively accelerated our roadmap. What we thought would take 6-plus months, we were testing in the first week of the beta program,” said Nate Chambers, chief product officer, ORCA Analytics, an AI-driven growth platform for e-commerce brands that consolidates marketing data, attribution, and LTV metrics into actionable dashboards. “They're shipping everything we need for agentic workflows, and launching new data connections faster than we can build them into our product. If you're building an AI product, you can stop rolling your own data pipelines and start shipping."

The platform is available today through the Model Context Protocol (MCP), which works inside Claude, ChatGPT, Cursor, and any MCP-compatible client, and through a native SDK for teams building custom agents from the ground up.

"Most agent projects stall for the same reason: The model is fine, the data is a mess. Five disconnected systems, inconsistent entities, no shared state," said Michel Tricot, co-founder and CEO of Airbyte. "Airbyte Agents gives every agent a unified view of the business, replicated and ready to query. That is what separates an agent that can do the work from one that just talks about it."

“Without Airbyte, we'd be stitching together bespoke data connectors for every integration, which would slow us down dramatically,” said Franziska Ibscher, head of product at Drivepoint, developer of a finance platform intended for scaling e-commerce and omnichannel consumer brands. “With Airbyte, our AI features have fresh, reliable data to work with. Whether we're running automated financial models or powering AI agents that answer questions about a brand's business, none of it works without trustworthy data flowing in, and that's what Airbyte gives us.”

Airbyte Agents is available two ways,

• Airbyte MCP: Connect data sources to Airbyte once, then build and run agents inside Claude, ChatGPT, Cursor, or any MCP-compatible client. No code required, and the same governed access to the Context Store that the SDK provides.
• Agent SDK: For teams building custom agents and applications directly against the Context Store, with full programmatic control over retrieval, permissions, and state.

The platform launches with 50 connectors that populate the Context Store, covering the systems most central to enterprise operations including Salesforce, HubSpot, Zendesk, Jira, and Slack. Airbyte's full catalog of 600-plus connectors will be available in the Context Store in the months ahead. A growing share of connectors also support write actions, letting agents update records, create tickets, and post messages in the systems of record. All connectors support OAuth-based authentication and row-level permissions, so agents only see what the invoking user is allowed to see.

Automations, a visual interface for building and running agents directly inside Airbyte, is also available in research preview. Built on the same Context Store as Airbyte Agents, it lets teams compose agentic workflows across connected systems without code, and will graduate to general availability in a later release.

Airbyte is offering existing customers three months of Airbyte Agents access with usage limits to support early adoption. Consumption is metered in Agent Operations, a unit that covers reads, searches, actions, and reasoning calls against the Context Store.

About Airbyte

Airbyte is the context infrastructure platform for AI agents and analytics. Airbyte Agents gives every agent a unified view of operational data through a replicated, search-optimized Context Store, a native SDK, and an MCP endpoint that works with any MCP-compatible client. Airbyte Agents is built on Airbyte's open-source data replication platform, powered by the industry's largest ecosystem of connectors and trusted by 7,000 enterprises to move structured and unstructured data across multi-cloud and hybrid environments. Data and AI teams use Airbyte to build both pipelines and agents on a shared foundation. For more information, visit airbyte.com.

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