Agents that actually finish tasks, not just start them
The difference between an impressive demo and something you'd trust unattended.
Most agent demos show the interesting 80% — the reasoning, the tool calls, the moment it figures something out. The boring 20% is what determines whether you'd actually trust it unattended: does it know when it's stuck, does it stop instead of guessing, does it leave a clean trail of what it did and why.
Narrow scope beats general capability
The agents that hold up in real use are the ones with a narrow, well-defined job and a hard boundary around it — not the ones with the most general capability. Scope the task tightly, give it explicit success and failure conditions, and make every action it takes auditable after the fact.
Design for the moment it gets stuck
Every agent eventually hits a situation its instructions didn't anticipate. What it does next is the real test: does it guess and press forward, or does it recognize the boundary of its own competence and stop cleanly. The second behavior has to be designed in deliberately — it doesn't emerge for free.
The unattended test
An agent that needs a human watching every step isn't automation, it's a slower way to do the same work with extra steps. Build for the version that runs while you're not looking, and design the guardrails, logging and stop conditions accordingly.
- A narrow, well-bounded task beats general capability for reliability.
- Explicit success and failure conditions matter more than clever reasoning.
- If it needs constant supervision, it isn't actually automation yet.
Evaluating LLM output like an engineer, not a fan
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