Artificial Intelligence has rapidly advanced before our eyes—first from rule-based systems that followed simple instructions, then to machine learning models that learned from data, and now to powerful large language models (LLMs) and generative AI platforms that can converse, create, and problem-solve at a human-like level.
I’ve seen this evolution not only as a technical milestone, but as a change in how we think about intelligence itself. For years, the focus was on building one model to do everything: the smarter the model, the more we expected it to solve on its own. But as organizations actually put AI into practice, especially in complex, multi-step processes like research, planning, customer interaction, or decision-making, a different reality has set in. No single model, no matter how advanced, can handle every nuance, every decision point, and every specialized skill that real-world tasks demand. Agentic orchestration represents the next evolutionary step in AI adoption. As enterprises shift from isolated tools to agent ecosystems,