AI agents are moving from demos to production—but only when teams treat them as systems, not chat widgets. That means clear boundaries, evaluation harnesses, and fallback paths when confidence is low.
The highest-value use cases we see today are operational: document processing, workflow routing, internal knowledge retrieval, and assistive coding inside defined guardrails. Each requires different latency, accuracy, and audit requirements.
Our recommendation: start with a narrow workflow, measure accuracy on real data, and design explicit human review for edge cases. Expand scope only after the system proves stable in production.