What is Agentic AI and How Does it Work?

Understanding Agentic AI Fundamentals

Agentic AI represents a paradigm shift in how artificial intelligence systems operate and make decisions. Unlike traditional LLM-based workflows that follow predefined paths, agentic AI systems possess the ability to autonomously reason about problems, select appropriate tools, execute actions, and adapt their strategies based on results. This video breaks down the concept of Agentic AI in accessible terms, making it clear for developers and AI enthusiasts who want to understand where this technology fits within the broader AI landscape. The tutorial progresses systematically from simpler AI architectures to more complex agentic systems, establishing a clear framework for understanding the distinctions and capabilities of each approach.

RAG-Based AI Systems Explained

Retrieval-Augmented Generation (RAG) represents one of the foundational approaches to enhancing LLMs with external knowledge. RAG systems work by retrieving relevant documents or data from a knowledge base when a user query arrives, then feeding that context to the language model to generate informed responses. This approach is powerful for scenarios where the model needs access to up-to-date or specialized information not present in its training data. However, RAG systems operate in a relatively linear fashion: retrieve context, pass it to the LLM, generate output. The system does not independently decide what to retrieve or iterate on its retrieval strategy based on intermediate results, which limits its autonomy and problem-solving flexibility compared to more advanced architectures.

Tool-Augmented AI Systems

Tool-augmented AI systems extend the capabilities of LLMs by allowing them to call external tools or APIs. Rather than relying solely on the model's internal knowledge, these systems give the model the ability to invoke functions, perform calculations, access databases, or trigger external services. A tool-augmented system might include weather APIs, calculators, database query functions, or payment processors that the LLM can invoke when needed. The key difference from simple RAG is that the model can actively request specific tools to accomplish subtasks. Nevertheless, tool-augmented systems typically follow a more rigid orchestration pattern where the available tools and their invocation logic are predefined by the developer, leaving less room for the system to autonomously decide on novel strategies or dynamic tool combinations.

How Agentic AI Systems Operate

Agentic AI systems introduce genuine autonomy into the decision-making process. An agentic system continuously evaluates its current state, reasons about the problem at hand, decides which tools or actions to take next, and iterates until it reaches a solution. The agent maintains an internal understanding of progress and can dynamically adjust its approach when intermediate results suggest a different path. Unlike tool-augmented systems where the orchestration is largely predetermined, agentic systems can compose multi-step workflows on the fly, retry failed actions with modified parameters, explore alternative tool combinations, and learn from the outcomes of each step. This reflective and iterative nature enables agentic AI to tackle complex, open-ended problems that require reasoning, planning, and adaptive behavior.

Building Agentic AI with Code

Implementing agentic AI systems programmatically involves creating an architecture where the core agent loop handles perception, reasoning, tool selection, execution, and feedback. Developers can build agentic systems from scratch using frameworks and libraries that provide the scaffolding for this loop, or leverage existing agentic platforms that abstract much of the complexity. The typical pattern involves defining the tools available to the agent, setting up the reasoning engine (often an LLM with specific prompting), and creating the execution and monitoring logic. Writing agentic AI from first principles offers full control and customization but requires deeper understanding of the architecture. The video demonstrates practical code examples that illustrate how these components fit together and how an agent makes decisions in real time.

Low-Code Agentic AI Platforms

For developers who want the benefits of agentic systems without building everything from scratch, low-code agentic AI platforms provide pre-built abstractions and visual interfaces. These platforms allow developers to define agents, tools, and workflows through configuration or drag-and-drop interfaces rather than extensive coding. Low-code approaches democratize agentic AI development by reducing the barrier to entry and accelerating time to production. Developers can focus on domain logic and tool definitions while the platform handles the agent loop, orchestration, and monitoring. However, low-code solutions often come with constraints in terms of customization and may not be suitable for highly specialized or cutting-edge agent designs. The video explores this middle ground, showing how teams can leverage low-code tools to quickly prototype and deploy agentic systems while maintaining acceptable levels of flexibility.

Distinguishing AI Agents and Agentic AI

An important clarification emerges between the terms "AI Agent" and "Agentic AI." An AI Agent typically refers to an autonomous entity that perceives its environment and takes actions to achieve goals, a concept with roots in classical AI and robotics. Agentic AI, in the modern context, refers specifically to the property of being agentic—possessing autonomy, goal-directed reasoning, and adaptive behavior. Not all AI agents are built using modern agentic AI techniques, and not all agentic AI systems are called "agents." The distinction matters for precise communication, especially when comparing legacy AI approaches with contemporary LLM-based agentic systems. Understanding this terminology helps developers and stakeholders align on what capabilities they are actually discussing and what architectural patterns are appropriate for a given problem.

Agentic AI in the Broader AI Evolution

Placing agentic AI within the evolution from general AI to generative AI shows how the field has progressed. Early AI systems were narrow and rule-based. Generative AI, driven by large language models, brought unprecedented capabilities in text, image, and code generation. Agentic AI represents a further evolution where generative models gain the ability to act autonomously, reason iteratively, and accomplish complex objectives through multi-step planning and execution. This progression does not make earlier approaches obsolete; rather, RAG, tool-augmented systems, and agentic AI each serve different use cases depending on the complexity of the problem and the degree of autonomy required. Understanding where Agentic AI sits in this landscape helps teams select the right architecture for their specific needs and anticipate where AI capabilities are heading as the field continues to mature.