Agentic workflows are AI powered systems that can plan and execute tasks autonomously.
Unlike traditional workflows that follow a predefined list of steps, that may fails if something unusual happens, agentic workflows work autonomouly to achieve the required outcome. If you want to learn more about the difference between traditional and agentic workflows, you can check the following blog: The difference between classic and agentic workflows
With the recent advancements in AI and LLMs, building agentic workflows has become more accessible. Here are some frameworks that can help you create your own agentic workflows:
Traditional automation handles simple and repetitive tasks very well, but struggles with complexity and change. Agentic workflows excel in dynamic environments where decisions need to be made quickly and problems require creative solutions.
Imagine a customer service system that doesn't just follow defined steps, but actually understands customer problems and finds the best resolution path. Or a research process that can gather information from multiple sources, analyze it, and generate reports all without constant supervision.
Agentic workflows don't just execute tasks, they understand context, solve problems, and continuously improve their performance if implemented correctly. They represent an evolution from rigid automation to intelligent collaboration.
This shift means organizations can handle more complex challenges, respond faster to changes, and free up human talent for strategic and creative work that truly matters.