Airbyte

OpenAI just launched an in-house data agent. This validates much of what I've been thinking about agentic data infrastructure! OpenAI has to operate across 600+ petabytes and 70,000 datasets. They cannot afford to let agents just endlessly query siloed data without control or optimization. The new internal tool includes some of the key elements I’ve discussed are required for production scale agentic products. Multiple replication layers (table metadata, human annotations, code-level enrichment), powering search that understands business logic, not just schemas. Closed-loop learning that remembers institutional knowledge. This isn't just OpenAI. Every company building serious agentic systems is hitting the same wall. Your agent can book flights or analyze sales, but only if the underlying data infrastructure supports cross-silo search with real understanding.

Schema changes are one of the most common causes of pipeline failures. Someone adds a column to a source table, and suddenly your Snowflake destination is throwing errors. This guide walks through detecting drift in Snowflake + propagating changes automatically. ⬇️ https://bit.ly/3LsUiro

Closed systems ask you to trust them. Open systems let you verify. Every action observable. Every decision traceable. Every access governed by clear policies enforced at the source. In regulated environments, this really matters. Control is what counts most. Michel Tricot breaks down why innovation moves too fast for any single company to figure out the best patterns alone: https://bit.ly/4bhENNo

Up to a 75% increase in performance. Zero cascading failures. One platform change. Symend was dealing with a critical flaw often found in legacy pipelines: when one job failed, everything downstream failed. Data delays rippled across all clients, and the engineering team spent their time firefighting instead of building. With Airbyte's distributed architecture on their side, Raman Singh and team eliminated the domino effect. Data refresh times dropped from 2 hours to as low as 30 minutes, and pipelines now run in parallel across multiple clients without impacting performance. This is what modern data infrastructure enables: reliable pipelines that scale without adding operational overhead. Read the full story: https://bit.ly/45oeWAm

Most agents forget everything between sessions. Teams tried to fix this with vector databases, but they don't scale for production use cases. The real problem? Treating memory as a single blob. Production agents need two types of memory: Session memory for the current conversation. High fidelity, temporary, exact wording matters. Long-term memory for what survives across sessions. Compressed, retrievable, evolves continuously. The bridge between them is summarization. When a session ends, extract what matters. A fifty message support conversation becomes three facts: issue reported, root cause found, resolution applied. Without this, you get unbounded growth. Ten thousand memories bury the signal in noise. Read my full thoughts below 👇

Every agent you develop eventually needs to talk to the outside world. 🗣️ The only problem with this is that building production agentic applications today that require reading from outside data means re-solving the same problems for each data source. Introducing Airbyte Agent Engine, which gives your agents a unified interface to external data sources. We're launching with 15+ agent connectors, including HubSpot, Salesforce, Gong, Linear, GitHub, and more, with hundreds more coming fast from Airbyte's native ecosystem of 600+ data connectors. Learn more: https://lnkd.in/ga9DEmHQ

By nature, AI agents are only as good as the tools they can call. Most enterprise context lives in SaaS APIs—CRM records, support tickets, code repos, billing events. But giving agents reliable access to these systems means building custom wrappers for every API. Airbyte Agent Connectors solve this: open-source Python SDKs that wrap operational APIs with strongly typed interfaces. Each connector handles authentication, validates schemas, and exposes operations through consistent methods that both developers and LLMs can interpret reliably. Check out how it works on GitHub: https://bit.ly/3Z341rC

Your work culture is keeping you from winning with AI. My co-founder Jean Lafleur explains it aptly: “The problem isn't that people don't want to adopt AI. It's that nothing in the system requires them to." Your team knows AI exists. But knowing and doing are separated by habit, comfort, and the absence of urgency. AI-native companies make NOT using AI socially awkward. They create public rituals where AI outcomes are showcased, not just tucked away on someone’s laptop. From executives downward, AI is a part of their organization's operating system. Sometimes, peer pressure is a good thing!

The Startup Grind Conference is just around the corner! If you're scaling, growing, and building a startup, don't miss this event with 175 insightful sessions from the leaders in the trenches.

If you're building AI agents, you know this pain: before long, you're managing OAuth flows, rate limits, and pagination for every single data source. All too often, your agent logic takes a backseat to integration plumbing. We built Airbyte Agent Engine to fix this. Connect your agents to Salesforce, GitHub, HubSpot, Linear, Gong and 10+ other sources in ~10 lines of code. You get fully-managed auth, plus an Entity Cache that runs complex queries in under half a second without hammering vendor APIs. Stop rebuilding the same integrations. Join the private preview: https://bit.ly/4pYeDmn