True agentic AI is years away - here's why and how we get there (www.zdnet.com)

The article highlights the current limitations of AI agents and the challenges ahead in achieving true agentic AI capable of transforming enterprise productivity. Despite the excitement surrounding generative AI applications, existing agents primarily function as simple automations, such as chatbots, and often fall short of complex, multi-step problem-solving. Recent market data indicates a preference for simpler co-pilot tools like ChatGPT Enterprise over more ambitious agent-based applications, underscoring that the technology needed to unlock the full potential of agents remains underdeveloped. Key challenges for the AI community include advancing reinforcement learning methodologies and developing new frameworks for AI memory management, as existing large language models (LLMs) struggle to retrieve and manage data over prolonged interactions. Research efforts, such as those by Stanford and DeepMind, are exploring ways to enhance agent capabilities through reinforcement learning and memory innovations. However, significant hurdles remain before AI can transition from reactive task-specific tools to fully autonomous agents capable of independent planning and strategic decision-making. The journey toward this vision is expected to take years of iterative technological advancements.