The field of artificial intelligence is rapidly evolving, with new concepts and technologies emerging constantly. Among these, agentic AI has garnered significant attention. This article aims to provide a comprehensive explanation of agentic AI, differentiating it from traditional chatbots and workflows. We will explore the core characteristics of agentic AI – goal-oriented planning, multi-step reasoning, and autonomous decision-making – and illustrate these concepts with real-world examples. The article will detail how agentic AI systems leverage tools, APIs, and knowledge to achieve complex goals, and contrast them with reactive and tool-augmented approaches. Practical examples, including AI coding assistants, travel planners, and equity research tools, will be presented, along with resources for building your own agentic AI systems.

From Chatbots to Agents: A Categorization

The discussion begins with a practical example from the speaker’s own company, focusing on the development of an HR assistant. Initially, the goal was to create a chatbot capable of answering simple policy questions, such as vacation day balances or sick leave procedures. This initial implementation involved retrieving information from PDF files and providing answers, a function that, while useful, does not constitute agentic AI. According to Anthropic’s framework, AI applications can be broadly categorized into workflows and agents. The initial chatbot is classified as a workflow – a reactive system responding to direct queries.

Augmenting Chatbots with Tools

The HR assistant was then enhanced to allow employees to apply for leave directly through the chatbot. This involved integrating the chatbot with the HR management system via APIs, enabling it to not only retrieve information but also perform actions. While this represents an improvement, it is still considered a tool-augmented chatbot, not an agentic one. It remains reactive, responding to specific requests rather than proactively pursuing goals.

Defining Agentic AI: Goal-Oriented Planning and Multi-Step Reasoning

The true leap towards agentic AI occurs when the system is given a broader goal, such as “prepare for Sarah’s maternity leave” or “onboard the new intern joining next Monday.” These tasks require multi-step reasoning and planning. The system must determine the necessary steps – scheduling meetings, creating profiles, ordering equipment – and execute them autonomously. This involves integrating with various tools – calendars, HR systems, IT support desks – and leveraging LLMs for tasks like generating meeting descriptions and creating tickets. This is the essence of agentic AI.

Key Characteristics of Agentic AI Systems

Several key characteristics define an agentic AI system. First and foremost is goal-oriented planning. Unlike simple task completion, agentic AI is given a complex objective and must devise a plan to achieve it. Second is multi-step reasoning, the ability to break down the goal into manageable steps. Finally, autonomous decision-making is crucial. The system should be able to perform actions – sending emails, scheduling meetings – without explicit instructions at every step. Access to tools, knowledge bases, and a memory component to maintain context are also essential.

Agentic AI vs. Reactive and Tool-Augmented Systems

To clarify the distinctions, a table summarizes the capabilities of each type of system. Reactive chatbots are purely question-and-answer systems, lacking tool usage or reasoning abilities. Tool-augmented chatbots add the ability to interact with external tools but still lack proactive planning and reasoning. Agentic AI, on the other hand, possesses all three characteristics: goal-oriented planning, multi-step reasoning, and autonomous decision-making.

Real-World Examples of Agentic AI

Several real-world examples illustrate the power of agentic AI. AI coding assistants, such as those offered by Lovable and Repel, can generate code for complex applications like Todoist, planning and executing the necessary steps autonomously. Travel assistants can book entire trips based on complex criteria, such as desired weather conditions and budget constraints. Equity research tools can generate comprehensive reports on companies, gathering information and analyzing data without explicit instructions.

Building Agentic AI Systems: Tools and Frameworks

Building agentic AI systems can be achieved through various tools and frameworks. Low-code/no-code platforms like Zapier and n8n allow developers to create complex workflows and integrate various tools. These platforms can be used to build reactive or tool-augmented systems, but with the addition of a reasoning model like GPT-4, they can be extended to create truly agentic AI systems. The Agno framework is another option, providing a foundation for building agentic AI applications with Python. The speaker provides code examples and resources in the video description to help viewers get started.

AI Agents vs. Agentic AI: A Clarification

It’s important to distinguish between AI agents and agentic AI. Agentic AI systems will always have one or more AI agents. These agents are the core components responsible for reasoning, planning, and decision-making. The diagram presented illustrates this relationship within the n8n framework.

Generative AI and Agentic AI: A Relationship

Generative AI, such as Google Gemini, is a crucial component of agentic AI, but it is not the entirety of it. Generative AI is responsible for generating text, images, or other data, but it is just one piece of the puzzle. Agentic AI leverages generative AI along with reasoning models, tools, and knowledge bases to achieve complex goals.

Resources for Further Learning

The speaker encourages viewers to explore the resources provided in the video description, including code examples for RAG and tool-augmented AI systems, and Anthropic’s guide on building effective agents. This guide outlines various design patterns for building AI agents, including workflow-based and orchestrator-based approaches.

In conclusion, agentic AI represents a significant step forward in the field of artificial intelligence. By combining goal-oriented planning, multi-step reasoning, and autonomous decision-making, agentic AI systems can tackle complex tasks and achieve goals without explicit instructions at every step. As the technology continues to evolve, we can expect to see even more innovative applications of agentic AI in various industries.