Most interactions with large language models (LLMs) are stateless. You send a prompt, you get a response. The model doesn't remember the previous conversation unless you explicitly include it in the next prompt. This is like talking to someone with no short-term memory. It's useful for simple tasks, but it breaks down when trying to solve complex, multi-step problems.

Agentic AI changes this. Instead of a simple input-output chain, agentic workflows create systems that can reason, plan, and act autonomously to achieve a goal. These systems maintain an internal state, allowing them to understand context, track progress, and adapt their strategy over time. We're moving from simple, linear prompt-chains to dynamic, state-aware graphs where the AI can navigate a complex series of steps on its own.

This cyclical process of thinking, acting, and observing allows an agent to tackle problems that would be impossible with a single prompt. It can use tools, search for information, and refine its approach based on the results of its actions.

To build these autonomous systems, developers rely on a set of powerful design patterns. These aren't rigid frameworks but rather conceptual blueprints for structuring an agent's thought process.

Two of the most fundamental patterns are ReAct and Reflection.

The ReAct pattern, short for "Reason and Act," is the engine of most agentic systems. Instead of just generating text, the LLM generates a chain of thoughts that lead to an action. It verbalizes its reasoning: "My goal is X. To achieve that, I first need to do Y. I will use tool Z to do it." Then, it takes the action, observes the outcome, and loops back to the reasoning step. This makes the agent's process transparent and allows it to course-correct if an action fails or produces an unexpected result.

The Reflection pattern adds a layer of self-critique. After performing a series of actions, the agent can pause and reflect on its progress. It analyzes its past actions, identifies potential mistakes or inefficiencies, and synthesizes its findings into a refined plan. This is like a person stepping back from a project to ask, "Is this the best way to do this? What have I learned so far?" This self-correction helps agents avoid getting stuck in repetitive loops and improves the quality of their final output.

For complex goals, an agent can't just react. It needs a plan. The planning and decomposition pattern involves breaking a large, ambiguous goal into a sequence of smaller, concrete sub-tasks. For example, if the goal is "plan a marketing campaign," the agent might decompose this into: "research target audience," "identify key marketing channels," "draft ad copy," and "create a campaign budget."

This plan can be created upfront or developed dynamically as the agent learns more about the problem. It provides a roadmap for the agent to follow, ensuring all necessary steps are completed in a logical order.

Of course, planning and learning are useless without memory. An agent needs to remember its plan, the results of its past actions, and key pieces of information it has learned. Long-term memory is what allows an agent to learn and improve across different sessions.

By combining these memory types, an agent builds a rich understanding of its environment and its own capabilities. This ability to plan, act, and learn is the foundation of Decision Intelligence—the capacity for a system to not just predict an outcome, but to evaluate complex trade-offs, choose an optimal course of action, and execute it effectively.

These patterns represent a significant step beyond simple chatbots. By designing systems that can reason, reflect, plan, and remember, we are building AIs that can function as true partners in complex problem-solving.