Deploying AI agents is not your typical software launch - 7 lessons from the trenches

The article summarizes industry lessons for building and running AI agents, stressing strong governance and observability, starting with narrowly scoped use cases, ensuring data quality, defining success metrics, and adopting AgentOps to manage the agent lifecycle; experts recommend granting autonomy proportional to reversibility, keeping humans in the loop, orchestrating multiple specialized agents instead of monoliths, and engineering for adaptability and robust context management to prevent scope creep, vendor lock-in, and performance degradation.