Breaking Down the Myths: Understanding Agentic vs. Non-Agentic Approaches in LLMs Through Simple Questions
Introduction
With the explosion of AI and LLMs (Large Language Models) in automating tasks, improving customer experiences, and developing intelligent systems, two key approaches are often discussed: the Agentic approach and the Non-Agentic approach. But what’s the real difference?
And more importantly, how can these approaches improve the systems you’re building? Does the Agentic approach really add value over the Non-Agentic method, especially if you’re already familiar with traditional coding and workflows? Let’s break it down clearly and compare these two approaches with an example you can relate to.
Non-Agentic Approach: The Traditional, Step-by-Step Way of Using LLMs
The non-agentic approach is essentially the traditional way of interacting with LLMs. You define specific prompts, queries, and responses. It’s a straightforward approach where you control exactly what the model does. You specify each step, and the model simply responds according to the instructions you give it.
Characteristics of Non-Agentic Approach:
Fixed Prompts and Sequential Steps
For every question or query, you provide a detailed prompt. If you’re handling multiple tasks, such as checking an order status, explaining a delay, and processing refunds, you’d need to explicitly define all these steps.
For example, you might first ask: “What’s my order status?” and based on the response, you might ask: “Why was my order delayed?” If the customer asks for a refund, you’ll need to define yet another set of prompts.
Manual Flow Control:
You have to program every potential step yourself. If the user asks something new, you need to write new code to handle that flow. It’s static because the system doesn’t decide what to do next on its own — it simply responds to the prompts you set.
Context Management:
Maintaining the context (e.g., previous interactions or data) isn’t automatic. You’d need to program the system to remember past interactions, such as checking an order’s status or processing a refund.
Agentic Approach: Making LLMs Smarter with Autonomous Agents
In contrast, the agentic approach involves creating agents that are designed to automatically take actions and manage workflows. Instead of just responding to prompts, these agents can decide what to do next based on the context, user goals, and the environment they are in. They also handle multiple tasks and interact with external systems (e.g., APIs or databases) without requiring manual programming for each step.
Characteristics of Agentic Approach:
Goal-Oriented Agents:
Instead of setting a prompt for each task, the agent has a goal (like “resolve order inquiries”) and decides what steps to take to accomplish that goal. For example, if the agent needs to answer a customer’s question about order delays, it knows to first check the order status, then explain the delay, and if necessary, initiate a refund process.
Dynamic Decision Making:
The agent doesn’t wait for you to dictate each action. It makes decisions based on the context and current situation. For example, if the customer asks about a refund after querying order status, the agent remembers the previous context and can automatically initiate the refund process.
Context-Aware:
The agent can track context between multiple queries. It doesn’t need you to write special code to remember previous interactions. This means if a customer asks about their order, then about the delay, then about a refund, the agent can use the context to handle all these requests in sequence without requiring manual intervention.
How the Agentic Approach Adds Value
The agentic approach adds significant value by making the system more adaptive and autonomous. While in a non-agentic system, you must pre-program every step for specific queries, the agent in an agentic system dynamically adapts to new situations.
For Example:
If a user asks, “What’s the status of my order?” and then asks about the delay, followed by a refund request, the non-agentic system would need separate, predefined prompts and responses for each of these steps. The agentic system, however, would seamlessly handle this entire flow. It would automatically check the order status, explain the delay, and then initiate the refund process without requiring you to manually code each step.
Moreover, in an agentic system, new queries can be handled more flexibly. Even if a user asks an unfamiliar question, the agent can dynamically decide what to do next, based on its understanding of the user’s context and available tools, like APIs, to fetch relevant data.
Q&A: Addressing Common Confusions Around Agentic vs. Non-Agentic Approaches
Q1: If I’m using a non-agentic system, can’t I simply predefine a flow for every possible user query like “What’s the status of my order?” or “Can I change my address?” Why do I need an agent for that?
In a non-agentic system, you can create predefined flows for specific user queries. However, you will still need to write out each possible sequence of steps for every interaction. If a user asks “What’s the status of my order?” the system would need to follow a fixed sequence, and if the user later asks for a refund, you’d need to code that separately.
An agentic system provides more flexibility. Instead of manually defining the sequence for each query, the agent automatically decides what to do next based on the context of the conversation. It doesn’t require every action to be predefined because it can handle new and dynamic scenarios without the need for you to code every possible sequence.
Q2: I understand that agents can choose actions based on context, but doesn’t that mean I still have to define the available actions, like APIs or tasks (e.g., updating an address)? Isn’t that the same as writing out the flow in the basic system?
Yes, for both agentic and non-agentic systems, you need to define the available actions (like APIs, external services, etc.). However, the key difference lies in how these actions are sequenced.
In a non-agentic system, you write the full sequence of actions for every interaction. For example, when the user wants to change their address, you must manually define all the steps involved (validate the address, check eligibility, etc.).
In an agentic system, the agent determines the order of actions based on real-time context. For example, if the user asks to change their address, the agent might first validate the address, then check if the user is eligible, and only then update the details. This reduces the need for predefining every possible action flow.
Q3: Can’t I just send user queries to the LLM and have it identify the topic (e.g., address change or refund request) to automatically pick the correct flow? How does an agentic system add value here?
In both agentic and non-agentic systems, the LLM can identify the topic (like “address change” or “refund request”). However, the difference lies in what happens next.
In a non-agentic system, once the topic is identified, the system will follow a hardcoded sequence of actions for that topic. If the user’s request changes or needs additional context, the flow could break, requiring more programming to handle these cases.
In an agentic system, once the topic is identified, the agent dynamically decides how to proceed based on the real-time context. The agent doesn’t just execute predefined steps; it considers the situation and adapts its response. For example, the agent might decide to validate a new address before initiating the update, something that would require additional logic in a non-agentic system.
Q4: Do I still need to program the available tools (like APIs) in the agentic system for tasks like address updates or sending emails?
Yes, you still need to define the available tools (APIs, email services, etc.) in an agentic system. However, the value of the agentic system lies in the decision-making process: the agent determines when and how to use these tools based on context.
In a non-agentic system, you must hardcode each step in a fixed sequence. In an agentic system, the agent can autonomously handle a variety of tasks (like verifying an address or issuing a refund) based on the user’s needs, without you needing to code the sequence of actions each time.
Q5: I understand that agents can decide what to do automatically based on context, but isn’t this still just a matter of writing predefined flows for each query? How does this differ from basic LLM prompts and static flows?
The key difference is that an agentic system allows for dynamic, goal-directed decision-making. While in a non-agentic system, you must define every step of the interaction, the agentic system enables the agent to autonomously determine the best sequence of actions based on the context.
For example, if a user asks to change their address, an agentic system might first check if the user is eligible and then validate the address, while in a non-agentic system, you would need to define every one of these steps manually.
By understanding the difference between the agentic and non-agentic approaches, you can choose the right strategy for your system. The agentic approach offers flexibility, context-awareness, and autonomous decision-making that greatly reduces the complexity and manual effort required in handling dynamic interactions, ultimately allowing you to build more scalable and efficient AI-driven solutions.
Conclusion
While agents can certainly help automate decision-making and reduce hardcoding, they still need predefined tools and some level of structured task flow, especially in more complex use cases. In simpler systems where you have a few interactions (like a change of address), you could hardcode the steps easily and achieve similar functionality.
However, the true benefit of agents becomes evident when the system grows and the number of potential interactions, tools, and goals increases. In such scenarios, dynamic decision-making — based on context — provides significant advantages over rigid, manually coded flows.
Why Is It Still Better Than Manual Flow Hardcoding?
The real benefit of agents comes when the system becomes more complex and dynamic. For instance, if you had hundreds or thousands of different queries related to a system (e.g., address changes, refund requests, order status), it would be impractical to hardcode all possible flows. Managing this manually would be cumbersome, error-prone, and time-consuming.
Agents reduce the need for manually creating separate flows for each possible scenario. Instead, they allow for the dynamic coordination of tools and tasks based on the goal and context. This flexibility not only speeds up development but also enhances scalability as the system grows.
Contextual understanding (e.g., remembering if an address has been changed before) is another key advantage of agents. This allows the system to act based on the user’s history rather than needing to constantly be reprogrammed with every new query. As systems evolve and require more sophisticated interactions, agents become an invaluable tool for managing complexity and providing a more responsive, user-centric experience.
In summary, agents offer a level of flexibility, decision-making, and scalability that manual hardcoding simply can’t match — especially when managing a growing number of dynamic interactions across diverse tools and goals.
Let me know your thoughts below and on my next blog lets code an agent from scratch.
