Patterns That Work and Pitfalls to Avoid in AI Agent Deployment

Highlights deployment patterns that consistently deliver value: start assistive then automate, use specialised multi-agent teams, and go event-driven Details common failure modes: unclear goals, over-promising capabilities, messy data, integration gaps, runaway token costs – and how to mitigate them Provides a checklist to stress-test agent projects before scaling, so you can avoid being part of the “cancelled by 2027” statistic