Why do most AI agents in real projects end up as “expensive automations” rather than truly autonomous systems?
We keep seeing “agents” that just chain API calls or prompts together. Curious where people think the real gap is — tooling, reliability, or the model itself?
Hi Tim — your point about reliability being the ceiling for real agent autonomy resonated. I’m working on a narrower problem underneath it: once an execution becomes stale, how do you actually remove its authority to create a downstream side effect? Curious how you’ve approached that boundary.
The gap is reliability. When you need a system that works every time in production, you end up constraining the agent so heavily it becomes an expensive if-this-then-that chain. The model is too unpredictable for true autonomy without a feedback loop that catches failures before they reach users. Most people skip building that loop because it is harder than chaining API calls. Tooling is getting better but the fundamental reliability ceiling has not moved much in the last year.