The field of Artificial Intelligence is rapidly evolving, and a key trend for 2025 is the rise of 'agentic AI.' This article aims to demystify this concept, providing a foundational understanding of how AI is moving beyond simple Large Language Models (LLMs) towards systems capable of more complex, autonomous workflows. We will begin by exploring how combining multiple LLMs – often referred to as a 'compound LLM' approach – can significantly improve output quality. We will then detail the three conceptual 'leaps' necessary to fully grasp the potential of agentic AI: reframing LLMs as 'agents,' recognizing that agents can be tools beyond LLMs, and envisioning orchestrator agents that dynamically manage workflows.

The Foundation: Compound LLMs

Traditionally, LLMs operate by predicting the next word in a sequence. Given a prompt – which can be multiple paragraphs of input text, instructions, and context – the model generates text one word at a time. While remarkably effective, this process lacks the iterative refinement inherent in human writing. Unlike a human who might create an outline, draft, revise, and seek feedback, an LLM typically produces a single output without the opportunity for self-correction or improvement.

A 'compound LLM' approach addresses this limitation by stringing together multiple LLM calls. Consider the task of creating a marketing plan for a new product launch. Instead of providing a single prompt and receiving a single response, a compound LLM might first generate a draft plan. This draft is then passed to a second LLM, tasked with critiquing the plan. Finally, a third LLM receives both the original draft and the critique, generating a revised plan incorporating the feedback. This iterative process, mimicking the human revision cycle, demonstrably improves the quality of the final output.

The First Leap: From LLMs to Agents

To truly understand agentic AI, the first conceptual leap is to reframe LLMs as 'agents.' Instead of thinking of a model simply generating text, consider it an entity performing a specific task. In the marketing plan example, one agent writes the draft, another critiques it, and a third refines it. This subtle shift in perspective is crucial for understanding the potential of more complex AI systems.

The Second Leap: Agents Beyond LLMs

The second leap is recognizing that not every agent needs to be an LLM. While LLMs are powerful tools for natural language processing, agents can also be simple tools or utilities. For example, an agent might be a Google search function, an API call to schedule an appointment, or a calculator. In the marketing plan scenario, an agent could be tasked with gathering relevant statistics to support the plan. This agent wouldn’t need to be an LLM; it simply needs to be able to perform a specific task and return the results. The ability to integrate diverse tools and utilities expands the capabilities of agentic AI significantly.

The Third Leap: Orchestrator Agents and Dynamic Workflows

The final leap involves envisioning orchestrator agents that dynamically manage workflows. Instead of predefining a rigid sequence of steps, an orchestrator agent can adapt to changing circumstances and make decisions about which agents to activate and in what order. Imagine an agent tasked with creating a presentation. This orchestrator agent could be instructed to write as many drafts as necessary, gather supporting data, and refine the presentation until it meets certain criteria. The orchestrator agent could even request feedback from other agents or human stakeholders. This dynamic approach, reminiscent of the interactive storytelling in shows like Black Mirror, allows for greater flexibility and adaptability.

Putting it All Together: Agentic AI in Action

Consider a scenario where an agentic AI system is tasked with creating a comprehensive report. The process might begin with an agent gathering data from various sources. Another agent could then analyze the data and identify key trends. A third agent might write a draft report, while a fourth critiques it. An orchestrator agent could manage this entire process, dynamically adjusting the workflow based on the data and feedback received. This system could even proactively request additional data or insights from human experts.

The Future of AI: Towards Autonomous Systems

Agentic AI represents a significant step towards more autonomous and intelligent systems. By combining the power of LLMs with diverse tools and dynamic workflows, we can create AI systems that are capable of solving complex problems and adapting to changing circumstances. As this technology matures, we can expect to see it applied to a wide range of industries and applications, from customer service and marketing to research and development.

The key takeaway is that agentic AI isn't just about building more powerful LLMs; it's about creating systems that can think, learn, and adapt like humans. By understanding the three conceptual leaps outlined in this article, you can begin to explore the potential of this exciting new technology and prepare your organization for the future of AI.