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Agentic AI: Building Intelligent Workflows [Guide]

Profile Picture of Guilherme Assemany
Guilherme Assemany
Senior Developer
An illustrated image depicting AI-agentic workflows. The image highlights AI agents turning user input into actionable outputs across digital tools.

AI agents are everywhere in conversations lately, and for good reason. Today, I’m excited to dive into this groundbreaking innovation: agentic workflows.

My personal interest in agentic workflows has grown in recent months, due in large part to a few articles I’ve written recently. One was a piece evaluating the real-world efficacy of AI agents (ChatDev, SWE-Agent, and Devin). The second was a deep dive into Devin AI, the tool I found most compelling from my initial research.

So, why are AI agents so fascinating to me? Unlike tools like ChatGPT, which follow a straightforward prompt-and-reply interaction model, AI agents are designed for more dynamic and context-aware engagement. Not only do they understand the broader context of a task, but can also plan strategies, critique their own outputs, and adapt their responses to user inputs in real time.

While this method of interaction is compelling on its own, I wanted to push the boundaries further and explore how agentic workflows can be used to create specialized AI tools. Tools capable of integrating with systems across your tech stack, perform complex, multi-step tasks, and deliver results that are ultimately more powerful than what traditional AI can achieve.

So, what exactly sets AI agentic workflows apart, and how do we go about building one? In this article, I’ll break down the key advancements that made agentic workflows possible, explore their real-world potential, and provide practical insights into how you can create and leverage these systems.

Table Of Contents

Key AI Advancements That Led to Agentic AI

To me, the current AI revolution feels a lot like the JavaScript framework gold rush of the mid-2010s. It felt like new tools and concepts were popping up daily, each claiming to redefine the game. What we didn’t always see then, however, was the years of innovation and behind-the-scenes experimentation that made these new JS frameworks possible.

The same thing is true today. AI agents haven’t evolved out of nowhere – there were some key innovations that enabled us to get where we are.

descriptions of 3 key advancements in ai that made agentic workflows possible
Three key advancements in AI that made agentic workflows possible.
Originally published on Jan 14, 2025Last updated on Jan 20, 2025
Key Takeaways

What is an agentic AI?

Agentic AI are systems that operate autonomously to achieve specific goals. They do this by making decisions and taking actions without constant human input. These systems form the foundation of AI Agentic Workflows, where their ability to self-reflect, plan, and collaborate with tools or other agents enables the automation of complex, multi-step tasks in dynamic environments.

What are agentic workflows in AI?

Agentic workflows in AI are structured processes that leverage autonomous AI systems, or "agentic AI," to handle complex, multi-step tasks. Unlike traditional AI systems that rely on direct user prompts, agentic workflows enable AI agents to self-reflect, plan, make independent decisions, and collaborate with tools or other agents. These workflows can integrate LLMs, retrieval-augmented generation (RAG), and multi-agent collaboration to create systems that adapt dynamically to challenges, execute iterative improvements, and deliver more intelligent outputs.

What is the difference between an LLM and Agentic AI?

The primary difference between a large language model (LLM) and agentic AI lies in their functionality and scope. An LLM, such as GPT-4, is a foundational AI model trained to process and generate human-like text based on a given prompt. The main drawback is that these tools operate within a prompt-response paradigm, and thus lack autonomy or decision-making capabilities.

An Agentic AI, by comparison, builds on LLMs (often using them as a core component), but extends their capabilities. Unlike LLMs, agentic AI systems can independently plan, self-reflect, collaborate with tools, and adapt to complex workflows. They are designed to achieve specific goals through multi-step, iterative processes without requiring constant user input.

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