Asking a large language model a question is a one-shot interaction. You send a prompt, you get a response. This works well for summarizing text or generating ideas. But for complex, multi-step problems, it falls short. A single prompt can't easily navigate unforeseen errors, gather new information, or adapt its strategy on the fly.

This is where agentic workflows come in. Instead of a single transaction, an AI agent operates in a continuous loop. It's less like a vending machine and more like a skilled worker who can assess a situation, make a plan, try something, see what happens, and adjust. This iterative process is the foundation of modern AI systems, allowing them to tackle tasks that were impossible with simple prompting.

This cycle is often described as a Perceive-Reason-Act-Reflect (PRAR) loop. The agent perceives its environment and the user's goal. It reasons about what to do next, breaking the goal into smaller steps. It takes an action using a tool, like searching the web or running code. Finally, it reflects on the outcome of that action to inform its next move.

This isn't just a linear sequence. It’s a dynamic cycle. The reflection from one step directly feeds the perception and reasoning of the next, creating a powerful feedback mechanism for intelligent behavior.

Several architectural patterns have emerged to structure these agentic loops. The most foundational is ReAct (Reasoning and Acting). The ReAct pattern elegantly interleaves thought and action. The agent explicitly verbalizes its reasoning—what it knows, what it needs to find out, and what its next step should be. Then, it takes a single action. It observes the result and loops back to reasoning.

For example, to answer "What team did the quarterback of the winning team in Super Bowl LVIII play for in college?", a ReAct agent's process might look like this:

While powerful, ReAct can be inefficient. For more complex problems, agents need more sophisticated strategies for planning and self-correction.

One such strategy is Reflexion. This pattern gives an agent the ability to look back on its failures and learn from them. Imagine an agent tasked with writing a Python script. Its first attempt fails with a TypeError. Instead of just trying again randomly, a Reflexion agent analyzes the error message (the observation). It reflects on what went wrong—"The function expected an integer, but I passed a string"—and adds this insight to its memory. It then re-runs the loop, using this new knowledge to generate a corrected script.

This is a huge step up from simple trial-and-error. The agent builds a short-term memory of what doesn't work, allowing it to solve problems more robustly.

Another key architectural choice is how planning and execution are related. The ReAct pattern blends them together in a tight loop. A different approach is the Plan-and-Execute model. Here, the agent first creates a complete, multi-step plan from start to finish. Only after the plan is finalized does it begin executing the steps one by one.

This is useful for tasks where the sequence of operations is predictable and doesn't depend heavily on intermediate results. The downside is its rigidity. If an early step fails or returns an unexpected result, the entire pre-made plan might become invalid.

This contrasts with more dynamic approaches like Tree of Thoughts (ToT), which explores multiple reasoning paths at once. If ReAct is like taking one step at a time and Plan-and-Execute is like following a map, ToT is like having several scouts explore different paths simultaneously and report back on which one looks most promising. It maintains a 'tree' of possible next steps, evaluates them, and proceeds with the most viable options, pruning the dead ends.

This makes ToT powerful for problems with vast solution spaces or those requiring creative problem-solving, but it comes at a higher computational cost than simpler patterns.

Understanding these agentic patterns is key to moving beyond simple AI toys and building robust, autonomous systems. The agent loop is the fundamental engine that drives modern AI, turning static models into dynamic problem-solvers.