Top AI Product

If you’ve been building anything serious with AI agents lately, you’ve probably run into the same ugly problem: agents crash, API calls time out, LLMs hit rate limits, and your carefully orchestrated workflow just… dies. That’s exactly the pain point [Temporal Technologies](https://temporal.io) has been quietly solving for years — and now the rest of the world is catching on.

Earlier this week, [SiliconANGLE](https://siliconangle.com/2026/02/17/ai-agent-reliability-startup-temporal-raises-300m-funding/) and [GeekWire](https://www.geekwire.com/2026/temporal-raises-300m-hits-5b-valuation-as-seattle-infrastructure-startup-rides-ai-wave/) both reported that Temporal closed a $300M Series D round led by Andreessen Horowitz, putting them at a $5 billion valuation. Sequoia, Lightspeed, and a bunch of other heavy hitters also joined the round. Those are not small names throwing money around for fun.

Here’s the thing — Temporal isn’t some overnight success riding the AI hype. The [open-source project](https://github.com/temporalio/temporal) has been around for a while, racking up over 18,000 GitHub stars and more than 20 million downloads per month. The core idea is what they call “durable execution”: your code runs, and if something breaks halfway through, Temporal remembers exactly where it stopped and picks right back up. No manual retry logic, no event sourcing headaches, no duct-taping together queues and pub/sub systems.

What’s really interesting is the customer list. OpenAI uses Temporal for production workflows. So do Netflix, Snap, JPMorgan Chase, and Block. When companies handling that kind of scale and that kind of sensitivity trust your infrastructure, it says a lot. The numbers back it up too — they reported 380% year-over-year revenue growth and a 350% jump in weekly active usage.

I’ve been watching the AI agent space closely, and the honest truth is that most agent frameworks focus on the fun part — the reasoning, the tool calling, the chain-of-thought magic. But nobody talks enough about what happens when things go wrong at 3 AM. Temporal fills that gap. It’s the boring-but-essential plumbing that makes the flashy stuff actually work in production. If you’re building AI agents that need to do real things in the real world, this is worth a serious look.