Shipping AI workflows clients actually trust
Automation only compounds if it's observable and reversible. Our checklist for putting LLM agents into production for growth teams.
The fastest way to lose trust in an AI workflow is to make it a black box. Every automated step should be logged, attributable, and easy to roll back.
We design agents the way we design UIs: clear inputs, predictable outputs, and a human in the loop wherever the cost of a mistake is high.
Before an agent touches production data, it runs against a suite of historical cases with known correct answers. If it can't beat the baseline on paper, it doesn't get to try in the real world.
Autonomy is earned gradually. New workflows start in draft mode, proposing actions for human approval, and only graduate to acting alone once weeks of proposals prove accurate. Trust is a ratchet, not a switch.
The payoff for the discipline is compounding: every logged run becomes training data, every correction sharpens the system, and the automation your team trusted with ten tasks this quarter earns fifty the next.