What is Agent Planning?
Agent planning is the process by which an AI agent breaks down a complex goal into a sequence of actionable steps, determines the optimal order of execution, and adapts the plan based on intermediate results.
WHY IT MATTERS
Complex tasks require planning. 'Rebalance this DeFi portfolio' involves checking current positions, analyzing market conditions, calculating target allocations, determining trade sequence, estimating gas costs, and executing transactions in the right order.
Planning approaches range from simple (ask the LLM to list steps before executing) to sophisticated (hierarchical task networks, Monte Carlo tree search). The plan-and-execute pattern — generate a complete plan, then execute steps one by one — often outperforms pure ReAct for complex tasks.
A key challenge is plan robustness. Real-world conditions change between planning and execution. Gas prices spike, prices move, transactions fail. Good agent planners re-evaluate and adapt their plans based on what actually happens at each step.
HOW POLICYLAYER USES THIS
When agent plans include financial actions, PolicyLayer validates spending before execution. If a plan would exceed budget limits, the agent learns this before committing — allowing it to re-plan within authorized boundaries.