The context layer for production-grade AI agents. Connect Salesforce, Stripe, Zendesk +50 more into a queryable Context Store, so your agent reasons across systems without stitching APIs at runtime. UI, MCP, or SDK. 40% fewer tool calls, up to 80% fewer tokens.
Hey PH 👋
Jean from Airbyte here. Five years ago we built Airbyte because moving data between systems was broken. Today we sync data for 20% of the Fortune 500. Over the last year we've watched dozens of those same teams build agent demos that look incredible, then fall apart the moment they hit production. Today we're launching what we wish they'd had: Airbyte Agents, the context layer for production-grade AI agents.
Why we built this:
Every team we talk to is trying to ship AI agents. The demos look incredible. Then they hit production and reality sets in:
- Engineers spend 4 to 6 weeks building each connector, and every API change resets the clock
- Multi-tenant OAuth, token rotation, and per-customer credential isolation become a permanent tax
- Agents get raw JSON from five different tools and no shared layer to help them figure out that "Acme Inc" in Salesforce is the same company as "acme.com" in Stripe
Most teams reach for MCP servers or roll their own. Those help agents reach tools. None of them solve the real problem: agents don't have the full picture before they act. That's the context engineering gap.
What Airbyte Agents does:
It's one system with three entry points:
- MCP Server: use Airbyte directly from Claude, ChatGPT, or Cursor
- Agent SDK: full programmatic control for engineers shipping agents into production
- Agents UI: build and operate no-code agents fast, with human-in-the-loop (in research preview)
All three sit on top of the Context Store, a unified operational layer for your business data. Every customer record, ticket, deal, invoice, and conversation from Salesforce, Stripe, Intercom, and the rest of your stack, synced and queryable in one place. The Context Store continuously replicates data so agents can search across systems without hammering APIs or blowing up context windows. We're already seeing 40% fewer tool calls and up to 80% lower token consumption.
We've spent the last year building this with early design partners shipping agents into production, on top of the same replication infrastructure trusted by 20% of the Fortune 500.
We’re launching with 50+ connectors including Salesforce, Slack, Linear, and more, with new connectors shipping weekly. Several connectors already support write, not just read.
On the roadmap: deterministic entity resolution, sub-millisecond search for the Context Store, TypeScript SDK, and CLI.
Try it free:
Our Free plan is available for all users. Airbyte customers get 3 months free access to our Team tier with the most advanced capabilities.
The product is early. We want to build it with you, because products improve faster with feedback from real users.
If you're shipping agents in production today, we'd love to hear where MCP servers and bespoke integrations are breaking down for you, and what's actually working. Drop a comment, we're reading every one.
I feel like a lot of these products are solving for invented solutions.
I had limited coding experience before starting to build my own enterprise apps + websites in Feb, and I have honestly not come up against I problem I have not been able to solve on my own.
By the time I knew N8n existed, I had already automated all of the ops in one of my projects.
I feel like having something of our the box for connectors could be useful but also as a discovery exercise, maybe part of your service could be to introduce new endpoints that customers previously had not considered (i.e. why not use X instead of Y).
The "context layer" framing is exactly what's missing in most production agents — the value isn't "call this API", it's "reason across systems without rebuilding the join at runtime." 80% fewer tokens is a serious unlock when you're deploying agents on noisy, time-sensitive data streams. We hit a parallel problem on the financial side: agents watching prediction markets and trading flows produce signal soup unless you give them a unified context of positions, baselines, and event history. That's basically what we built into PolyMind for Polymarket alerts. Curious how Airbyte's Context Store handles temporal data — do you snapshot events with timestamps so an agent can reason about "what changed since 9am" without re-pulling raw API history?
About Airbyte Agents on Product Hunt
“The context layer for production-grade AI agent”
Airbyte Agents launched on Product Hunt on May 5th, 2026 and earned 82 upvotes and 4 comments, placing #17 on the daily leaderboard. The context layer for production-grade AI agents. Connect Salesforce, Stripe, Zendesk +50 more into a queryable Context Store, so your agent reasons across systems without stitching APIs at runtime. UI, MCP, or SDK. 40% fewer tool calls, up to 80% fewer tokens.
Airbyte Agents was featured in Productivity (651.7k followers), Developer Tools (512.4k followers) and Artificial Intelligence (468.5k followers) on Product Hunt. Together, these topics include over 294.5k products, making this a competitive space to launch in.
Who hunted Airbyte Agents?
Airbyte Agents was hunted by Garry Tan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Airbyte Agents stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Garry Tan