Durable AI workflows

The engineering problem

An AI workflow can outlive the browser session that started it. A useful backend has to make sense of that situation: whether work was accepted, what is still running, and where a completed result can be found. Getting an answer from a model is only part of the product experience.

My work focuses on this execution layer. I designed and implemented lifecycle APIs and the supporting backend behavior for long-running agent work. The platform is in development, where I am working through reliability and integration concerns before broader use.

My contribution

I translated the lifecycle into concrete API behavior and implementation. That included handling submissions, recording progress, making terminal outcomes explicit, and supporting recovery when a client reconnects. I also wrote lifecycle tests and coordinated the boundaries between backend components.

A key requirement was that a completed result remain accessible independently of a live connection. This is a useful way to evaluate an architecture: start with the behavior a person needs, then ask which parts of the system must preserve it when a process or connection fails.

The work involved deciding what belonged in the current release and what needed more design or validation. Clear scope helped keep the implementation reviewable and the remaining questions visible.

What I take from it

I enjoy the point where distributed systems design becomes a product guarantee. Terms such as retry, completion, and recovery seem straightforward until several components can observe the same work at different times.

I now give those terms explicit definitions early in a design. They make implementation discussions more productive, tests easier to interpret, and operational problems easier to explain. This project has also reinforced the value of distinguishing an implemented behavior from one that has been exercised under realistic failure conditions.