Spinning up an AI agent has never been easier. With a goal in mind, a few minutes of your fingers flying across the keyboard, and a handful of APIs, you’ll have something that will make your fellow devs’ eyes sparkle in a demo.
The bigger question is: will it provide value for your users?
What looks good in the demo often masks much deeper issues.
The reliability? Shaky.
The observability? 🤷
The state management? Messy.
The development complexity? Quite overwhelming.
But, we’re curious. And we’re always eager to learn more about the software industry and the developers who fuel it, so we conducted research with the goal of understanding how today’s engineering teams navigate AI challenges.
To achieve this goal, we surveyed more than 150 developers and technical leaders across a wide range of industries. We dug into the tools, languages, and infrastructure behind all their AI projects and figured out where the cracks show up and, as a result, what a production-ready AI stack needs to survive.
The story is honestly pretty straightforward: today’s AI tooling makes it easy to build prototypes, but nearly impossible to build production systems with actual value.
This report serves as the guide to help you bridge the gap between an AI prototype and a system that delivers business value.
Key Takeaways
- What the right tech stack looks like
- How your peers work with AI
- How Durable Execution fits in
- Production AI that lasts
