What Is an Agentic Workflow? | Forkast Learn

What is an Agentic Workflow?

An agentic workflow is a design pattern where an AI system does not just provide a single, immediate answer. Instead, it plans, executes, reflects on, and iteratively improves its approach to complex tasks. Unlike single-shot inference—where an AI generates a response in one go—an agentic workflow allows the system to maintain context across multiple steps, use external tools, and catch its own mistakes.

The Chef Analogy

Think of the difference between a cook following a rigid, single-step recipe and a professional chef preparing a complex meal. A rigid recipe is like standard AI: you follow the instructions exactly, and if something goes wrong, you are stuck. An agentic workflow is like the chef. The chef plans the meal, executes each step, tastes the food to see if it needs more seasoning (reflection), and adjusts the plan if the dish isn’t coming together correctly. This ability to adapt is what makes the process truly intelligent.

How the Loop Works

At its heart, this process relies on a continuous loop of improvement. First, the AI performs planning, where it breaks a large goal into smaller, manageable subtasks. Next, it moves to execution, where it uses function calling and tool use to interact with software or data. Then comes reflection, where the AI critiques its own work against quality standards. If the goal remains unmet, the system will re-plan and try again. This cycle is often powered by chain-of-thought reasoning, which helps the model map out its logic before acting.

Foundational Patterns

Several research breakthroughs have shaped how we build these systems. The ReAct paper by Yao et al. introduced a foundational pattern that interleaves reasoning with specific actions, allowing the model to observe the results of its work in real-time. Building on this, the Reflexion framework by Shinn et al. adds a layer of memory, storing past self-reflections to improve future attempts. These methods move beyond simple prompt engineering by creating a structured environment for the AI to learn from its own process.

Workflows vs. Agents

It is helpful to distinguish between the structure and the actor. As noted in Anthropic’s guide on building agents, workflows are often predefined code paths that orchestrate how an LLM behaves. In contrast, an AI agent is an LLM that dynamically directs its own processes. While they are closely related, the workflow provides the map, while the agent provides the decision-making power.

Why It Matters

By moving away from one-off responses, agentic workflows allow AI to handle tasks that require reliability and nuance. Because the system can verify its own output and adapt to new information, it becomes far more capable of solving multi-step problems that would otherwise baffle a standard model. This shift toward agentic AI is a major step in making these systems more autonomous and dependable.

Related Concepts

To build these systems, developers often use orchestration frameworks like LangGraph, AutoGen, or CrewAI. These tools provide the infrastructure to manage complex interactions. Other common patterns include the Plan-and-Execute model, where a planner generates a full roadmap for an executor, and the Evaluator-Optimizer pattern, where one model generates content while another critiques it. Regardless of the specific approach, the goal remains the same: using agent memory and iterative loops to achieve better results.