Reliability Over Generality
Demos work 60% of the time; enterprises need the nines. Prioritize reliability over everything, then climb the abstraction ladder.
The critical path is to build agents that operate at a higher and higher level of abstraction over time while holding an insanely high reliability standard — that is what turns research into something customers want, and the resulting usage teaches you how to reach the next abstraction faster. Flashy agent demos work ~60% of the time; enterprises cannot use anything below the nines (one use case ends with a physical truck being dispatched). There is a real reliability-vs-generality-vs-cost-vs-speed trade-off; you push the Pareto frontier by framing every use case as "collect more data," not by being prescriptive about the model's end steps.
- Reliability before generality: hit the nines first, then raise abstraction.
- High reliability at a given abstraction generates the data to unlock the next.
- Beat the reliability/generality trade-off by making every use case look like more data, not more hand-coded rules.
Adept's experience shipping ACT-1-style agents into enterprises, where the prompt-engineering agent playbook could not clear the reliability bar.