Planning as the Core Challenge in Agentic AI: Solving it with Reinforcement Learning

Picture a team of AI agents working together seamlessly to tackle a complex business strategy problem — one agent researching market trends, another analyzing financial data, and a third crafting recommendations, all coordinating their efforts towards a common goal.

This logic of collaborative artificial intelligence, known as agentic AI, represents the next frontier in automation and problem-solving. As AI systems become more sophisticated, there is growing interest in moving beyond rigid, predefined processes to embrace flexibility, adaptation, and teamwork among AI agents.

Agentic AI holds immense promise for automating intricate, open-ended tasks that have long resisted traditional automation techniques. By breaking down complex problems into specialized roles and leveraging the unique capabilities of individual AI agents, multi-agent systems can orchestrate intelligent automation in ways that were previously unimaginable. Pioneering frameworks like CrewAI, Langraph, and Autogen are paving the way for this new paradigm, enabling developers to design and deploy crews of AI agents that can autonomously navigate and execute complex workflows.

However, as we venture into this new territory of collaborative AI, we encounter a fundamental challenge that lies at the heart of agentic systems: planning.

How do we enable AI agents to effectively plan their actions, coordinate with each other, and…