AI agents are only interesting when they can operate inside a real business context. That means bounded actions, explicit state, and enough observability to understand what the system did and why.
Start with the workflow, not the model
Most production failures happen because the workflow is vague. Before adding agent logic, map the input, the decision, the fallback path, and the human review point.
Design for reviewability
If an agent action matters, the outcome should be reviewable. Logs, events, and clear step history matter more than clever prompts.
Keep the integration layer simple
The best systems expose a narrow set of actions to the model and keep the rest of the logic in the application layer. That keeps the behavior stable as the underlying model changes.
Production is mostly about controls
An agent system survives when it has rate limits, fallbacks, permission boundaries, and a clear way to stop or redirect behavior when the context is uncertain.