The viral agent demos promised software that does everything. What actually ships are agents scoped to one job with tight guardrails, and that narrowing is the point.
A quick read — the essentials, fast.
The agent demos that went viral all looked the same: tell an AI a vague goal, watch it browse, click, write, and orchestrate its way to a finished result with no human in the loop. The demos were genuinely impressive. The products that actually ship look almost nothing like them. Real deployed agents are narrow, scoped to a single job, and wrapped in guardrails the demos never showed. That gap is not a failure. It is the whole lesson.
Why the open-ended demo breaks in production#
An agent that takes many steps toward an open goal has a reliability problem that compounds. Each step carries some chance of going wrong, and errors stack. A small failure rate per action becomes a large failure rate across a long chain. Twenty steps that are each highly reliable can still add up to a coin flip on whether the whole task succeeds. In a demo you cherry-pick the run that worked. In production you have to handle the runs that did not, and there are more of those than the demo suggested.
Worse, errors do not just accumulate, they cascade. One wrong step sends the agent down a path where every later decision is based on a faulty premise, and it confidently keeps going. By the end it has produced something that looks plausible and is entirely wrong.
The deeper issue is that open-ended agents are hard to trust precisely because they are open-ended. When the scope is everything, the failure modes are everything too, and nobody can reason about what the system will do next. You cannot test a space you cannot enumerate.
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