My focus is what happens beyond the happy path.
Looking past the happy path is only worth something if the way the work is done can be repeated and checked. Four habits keep it that way.
If a system can take action, its actions must be traceable, its failures diagnosable, and its execution controllable.
Understand what breaks before deciding what to build.
Every engagement starts with the actual problem: a failed workflow, an unreliable agent, or an automation that cannot be trusted in production. The goal is not to add AI to a process. It is to identify the failure, understand its cause, and build the right solution.
One responsibility. Explicit tools. Defined boundaries.
An agent that does everything is difficult to reason about, test, and control. I design systems around clear responsibilities, minimal tool access, and explicit execution boundaries. When something fails, the cause should be traceable, the fix should be contained, and the rest of the system should keep working.
Autonomy with explicit limits.
Every agent operates within defined budgets, timeouts, iteration caps, and tool permissions. Sensitive actions require approval. Every execution leaves a trace. Routine work runs autonomously; consequential actions stay behind explicit control gates. Autonomy should expand only as far as the system can reliably support it.
If you cannot evaluate it, you cannot trust it in production.
Every run should produce evidence: traces, outcomes, failures, and measurable quality signals. Evaluations become regression tests that catch degradation before deployment, not reports that nobody reads. AI-generated output is not finished when it looks correct. It is finished when it has been tested, its failure modes are understood, and someone takes responsibility for what ships.
No match.