Deploying AI agents is not your typical software launch - 7 lessons from the trenches (www.zdnet.com)

A recent article from MIT Sloan Management Review highlights the distinct challenges and lessons learned from deploying AI agents, emphasizing that the process diverges significantly from traditional software launches. Key takeaways include the importance of governance—implemented from the outset to avoid costly retrofits—and the need for organizations to meticulously assess trade-offs in autonomy for these agents. As AI agents gain traction in various sectors, industry experts stress that their success hinges not only on technological capabilities but also on sound operational frameworks, like 'AgentOps,' which manage the entire lifecycle of AI deployments. Additionally, practitioners recommend starting with narrow applications, ensuring high data quality, and framing efforts around specific problems rather than just technology. Observability, adaptability, and maintaining context are critical factors for success; for instance, managing how AI models prioritize information is crucial to preventing loss of relevance. Overall, as organizations explore integrating AI agents into their workflows, thoughtful governance, clear outcomes, and continuous iteration are underscored as vital for avoiding premature failures and maximizing the transformative potential of these technologies.