Show HN: Airbyte Agents – context for agents across multiple data sources
Airbyte just shipped a data layer that cuts agent token consumption by up to 90%. Here's why that matters for production AI workflows.
Why it matters
As AI agents move into real business workflows, they're failing because they lack structured access to multi-system data. Airbyte Agents solves this by pre-indexing operational data, dramatically reducing the token overhead and reasoning steps required for agents to answer cross-system queries.
The key facts
7 to knowAirbyte launches Airbyte Agents — a unified data layer for multi-source agent discovery and action
Real-world case: agent answering 'which customers are at risk of leaving?' required 47 steps; with Airbyte Agents, context is pre-indexed
Token consumption benchmarks: 80% fewer tokens vs Gong MCP, 90% fewer vs Zendesk, 75% fewer vs Linear, 16% fewer vs Salesforce
Core innovation: Context Store — agentic search index populated by Airbyte's 6-year library of data connectors
Solves agent discovery problem: agents can now search structured data before querying APIs, vs. inheriting weak API primitives
Public benchmark harness released for community validation
Target use cases: cross-system queries (enterprise deals + support tickets, support tickets without Github issues, etc.)
Go to the source
Hacker Newsnews.ycombinator.com
Publisher excerpt: I’m Michel, co-founder and CEO of Airbyte (https://airbyte.com/). We’ve spent the last six years building data connectors. Today we're launching Airbyte Agents (https://docs.airbyte.com/ai-agents/), a unified data layer for agents to discover information and take action across operational systems.…