Designing AI agents that actually finish the job
Most agent demos loop, stall, or wander off. Shipping agents that complete real workflows is about state, tools, validation, and knowing when to ask a human.
An agent that plans beautifully and then loops forever is worse than a script. Getting agents to reliably complete multi-step workflows in production comes down to a few disciplines that rarely make the demo videos.
Model the workflow as an explicit state machine
Free-form 'think then act' loops are hard to debug and easy to derail. Modelling the workflow as an explicit graph of states — with defined transitions — makes the agent's behaviour legible, testable, and recoverable when a step fails.
Validate every tool output
Tools return the real world, and the real world is messy. Validating each tool result against a schema before the agent acts on it catches half the failures that would otherwise cascade into a wrong final answer.
- Explicit state beats implicit loops for reliability and debuggability.
- Schema-validate tool outputs before the agent reasons over them.
- Design the human-in-the-loop fallback first, not last.
Know when to hand off to a human
The mark of a production agent isn't that it never gets stuck — it's that when it does, it hands off cleanly to a human instead of guessing. Designing that fallback from day one is what makes an agent safe to put in front of customers.
A great agent isn't the one that never needs help. It's the one that knows when it does.