Systems | Development | Analytics | API | Testing

By Matt O'Riordan, CEO and Co-Founder Across AI infrastructure right now, one word is doing a lot of work: durable. It is attached to execution. To agents. To workflows. To sessions. To streams. To transports. To memory. Every few weeks, another product ships with "durable" in the name. This is not branding noise. The underlying observation is the same in every case. AI systems are long-lived. They can fail at any layer. They need infrastructure that assumes failure rather than hopes against it.

Python dominates AI development. It's where teams build their agents, orchestration layers, and the backend systems that turn LLM calls into products people actually use. Over the past year, those systems have matured rapidly. What used to live in notebooks and prototypes is now running in production, serving real users with real expectations around reliability and performance. That maturity brings infrastructure requirements. Tokens need to stream in order.

Over the past year, I've spoken to more than 40 engineering teams building production AI agents. Different companies, different frameworks, different use cases. The same conversation kept happening.

You start a research task on your laptop, the network drops during a meeting, and when you open your phone to continue, the conversation is gone – you re-prompt, get partial duplicate results, and lose 30 minutes of work. The delivery layer dropped it. That's one of the most consistent problems teams hit when building AI applications. It's particularly acute in customer support, where a session belongs to the conversation - not to any single device, connection, or participant.