Agentic AI for Multi-Step Tasks

Instead of giving an AI a single prompt and expecting a perfect, complete output, think of it as an 'agent' that can break down a big task into smaller steps, decide which tools to use, and even correct itself. For instance, if you want an AI to plan a marketing campaign, instead of asking for the whole plan at once, an agentic AI would first brainstorm target audiences, then research competitor strategies using a search tool, then draft ad copy, and finally ask for your feedback before refining the plan. This allows for more complex, dynamic problem-solving than a simple prompt-response model.