Airbyte

@AirbyteHQ

The data and action layer for AI agents

San Francisco, CA

airbyte.com

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Airbyte
@AirbyteHQ
May 5
Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge
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Airbyte
@AirbyteHQ
Sep 10
New on Airbyte Pro & Enterprise Flex: data worker capacity charts with real utilization data (up to a year of history) + immutable audit logs for compliance.
What's New: Data Worker Capacity Charts and Audit Logs | Airbyte
From airbyte.com
Airbyte
@AirbyteHQ
Sep 8
Model costs drop 10x/year. Context compounds the other way. Every entity and data stream you architect into a knowledge layer keeps paying out as models shift. Own it, don't rent it. New piece by @MichelTricot
Your Context Compounds. Your Model Doesn't | Airbyte
From airbyte.com
Airbyte
@AirbyteHQ
Sep 7
Destination Databricks 4.0: up to 4.5x faster, less warehouse compute. Protobuf transport, no raw tables, typed Avro staging.
airbyte.com
Databricks 4.0: 4.5x Faster Direct Loading | Airbyte
Destination Databricks 4.0 is up to 4.5x faster with Direct Loading, Speed Mode, and Avro staging, cutting sync time and warehouse compute costs.
Airbyte
@AirbyteHQ
Sep 3
@MichelTricot sat down with @tfir_io on why APIs built for humans break AI agents 🎬 Agents need discovery, cross-referencing, and write-back across dozens of systems. Existing APIs were designed for narrow automations at low scale. Thin MCP wrappers don't fix that gap. His