The most useful agents are not the ones that simply complete more tasks. They are the ones that know what they are doing, why it matters, what could go wrong, and when not to act. That is the work I have been doing recently: turning agents from task executors into systems with situational awareness. A task agent asks, “What did the user ask me to do?” A situationally aware agent asks a better set of questions: ...
Most agent demos start in the wrong place. They show the model doing something impressive. It reads a ticket. It writes code. It opens a pull request. It passes a test. For a few minutes, it feels like the future arrived early. Then you try to use the same idea in a real system. The task gets interrupted. The model loses context. The test suite flakes. The branch goes stale. A token expires. A tool returns partial data. A dependency changes. The agent keeps retrying the wrong thing. Nobody knows whether it is stuck, still working, or about to break something expensive. ...
If you want reliable work from AI systems, the spec matters more than the prompt. A prompt is a request. A spec is shared reality. That distinction changed how I build. Most AI coding advice obsesses over prompts. Wording. context size. model quirks. That matters a little. It matters less than having a clear unit of work. The bottleneck is usually not model intelligence. It is ambiguity. A weak prompt can still work on a small task with a strong reviewer. But once work spans multiple files, tools, sessions, or handoffs, prompts get lossy fast. The agent improvises. The human re-explains. Review slows down. Rework stacks up. ...