You implement planning and reasoning in Agentic AI by explicitly separating decision-making from execution and putting both inside a controlled loop. The core idea is simple: the agent receives a goal, reasons about the current state, plans the next action, executes it using tools or APIs, observes the outcome, and then decides what to do next. Planning is the act of breaking a goal into steps, while reasoning is the evaluation of whether those steps are correct, sufficient, or need adjustment based on new information.
In practice, this usually means maintaining an explicit agent state object. That state includes the goal, known facts, intermediate results, errors, and constraints. The language model is prompted to propose the next action based on this state, often in a structured format such as JSON. Your application code validates that action, executes it, and updates the state. Reasoning improves when the agent can look back at prior attempts, retrieved context, or similar past tasks. This is where memory retrieval becomes important: relevant past decisions, logs, or documents can be retrieved from a vector database such as Milvus or Zilliz Cloud and injected into the reasoning step.
For production systems, planning and reasoning must be bounded and observable. You should enforce limits on the number of steps, tool calls, and execution time. Add checkpoints where the agent must justify its next action in plain language before proceeding. Log every plan, action, and outcome so failures are debuggable. Start with shallow planning (one step at a time) and expand only when needed. Agentic reasoning is not about letting the model “think forever,” but about designing a loop that steadily moves toward a goal while remaining predictable and auditable.