🧠Inside AI 16/♾: Planning , How Agents Break Big Problems Into Steps Intelligence isn’t just answering , it’s organizing. Planning is the layer that lets agents turn vague goals into structured, executable steps. Here’s how AI planning actually works 👇 1) Goal Understanding An agent first rewrites the user request into a precise objective with constraints, success criteria, and assumptions. This is where ambiguity gets removed. 2) Decomposition The planner breaks the goal into smaller tasks: research → decisions → writing → verification → final synthesis. Each subtask becomes a node in a directed workflow graph. 3) Ordering & Dependencies Tasks are arranged into a sequence or DAG: some steps must finish before others; some run in parallel; some repeat until quality stabilizes. 4) Tool & Agent Assignment The planner chooses which specialist handles which task , researcher, coder, critic, summarizer and which tools are needed. 5) Replanning Loops If a step fails, contradicts memory, exceeds limits, or produces low-confidence results, the planner adjusts the plan and tries again. This is where real “agent intelligence” emerges. Planning is what turns an LLM’s raw ability into structured, goal-driven behavior. Without planning, agents are reactive. With planning, they become problem-solvers. Which planning concept is best : decomposition, DAGs, or replanning loops? #InsideAI #AgenticAI

“Innovation is deeply embedded in Morgan Stanley’s DNA and crucial to driving our business forward,” said Robert Rooney, Morgan Stanley’s Global Head of Technology as he kicked off Morgan Stanley’s exclusive TechWeek & CTO Innovation Summit in Palo Alto, California.