How to build AI agents that don’t break at scale (www.techradar.com)

The Chief Evangelist at Kore.ai highlights critical challenges organizations face when scaling AI agents beyond pilot projects. As successful early trials create an illusion of readiness, many companies encounter issues such as unclear goals, inadequate data foundations, and lack of transparency, leading to potential failures in their AI initiatives. Gartner's projection that over 40% of AI projects may be canceled by 2027 underscores the importance of defining specific tasks, ensuring AI-ready data, and embedding transparency and security from the outset, particularly as agents handle sensitive information. For the AI/ML community, these insights emphasize the necessity of treating AI agents as dynamic systems that require ongoing evaluation and adaptation to shifting business needs. Leaders are urged to prioritize seamless integration with existing workflows and to proactively address governance and risk management. By adopting a strategic approach to building and maintaining AI agents, organizations can enhance their chances of long-term success and maximize the value derived from AI technologies.