Superagent is a new AI research assistant that works like a coordinated team, not a single bot. Ask a complex question—like a market, competitor, or investment analysis—and it plans the work, pulls from trusted data sources, and delivers a polished, interactive report you can use immediately.
Today, we’re announcing Superagent, our first standalone product built on multi-agent coordination. When you ask Superagent a question, you're not getting one AI assistant doing sequential tasks. You're getting a coordinating agent that plans the work, deploys specialists who work in parallel, and synthesizes their output into a finished deliverable you can immediately use.
This approach reflects what I call 'inside out' product thinking: start with what's now technically possible at the frontier of technology, then work backward to create the right product experience. Multi-agent systems represent that frontier today.
Superagent is built on our acquisition of DeepSky and reflects our broader momentum in AI – from hiring David Azose as CTO after he led ChatGPT's business products at OpenAI, to launching Airtable for ChatGPT, to refounding as an AI-native platform.
The concept is strong but I do wonder about transparency. My hesitation would be not knowing how each part of the work was produced.
I am curious how much control users have during the process. My concern is whether I can guide direction once the agents start working.
Hello team, I came across this while browsing and paused to read more. My research tasks usually take longer than expected. This feels like a different approach.
Happy to see this launch today. Turning complex questions into usable deliverables is not easy. I hope this helps reduce busy work for many teams.
This looks interesting overall. I feel showing a concrete example report could help people like me understand how polished the final output really is.
@Superagent from Airtable amazing guys, i'm a huge fan of Airtable for the last 3+ years, and I'll definitely try Superagent. What are the best use cases to start with if I'm a startup founder?
We’re building a multi-agent system at Nuomy too, and our research shows exactly what you’re addressing—users are getting tired of juggling prompts. They just want the final deliverable without having to manage the AI.
This inside-out approach is definitely the way to go, but I’m curious: what was the biggest challenge in making the coordination layer reliable? How do you handle it when sub-agents find contradicting info or get out of sync?
Announcing Superagent, from @Airtable CEO@howietl: