AI has been going through a profound transformation. And, it will continue to do so in 2025 and beyond, undoubtedly. We have already moved from static single models and reactive systems to proactive, intelligent agents, capable of handling complex tasks across multiple domains. By the way, what are these AI agents and how do they work?
To start explaining that, we need to understand the evolution of AI and the type of AI agents that exist today. In this article, we’ll focus on:
- AI Evolution (major shifts that occurred)
- How do AI agents work
- Detailed examples to better understand AI agents
- Why should you care about AI
This deep dive explores the functionality and potential impact of AI agents, tracing their evolution from simple reflex agents to multi-agent systems capable of coordinating multiple agents to accomplish specific tasks.
Alright, let’s dive in.
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The Evolution of AI Models
Shift from Monolithic Models to Compound AI Systems
“Monolith’ means a substance of one piece. It’s the same context in software, too. Monolithic models in AI refer to centralized, self-contained systems designed to handle multiple tasks through a single framework.
Once, the development of software was completely dominated by the monolithic architecture. But, now these are increasingly replaced by modern systems.
AI systems from 2020 to 2022, such as GPT-3, were monolithic models that used broad knowledge to perform different tasks. These systems processed inputs based on pre-trained patterns and did not integrate real-time external data and real-world workflows. Training these models required enormous datasets and resources, yet their ability to manage complex tasks was still constrained.
If you had to train these models, it would take a significant investment in data, and resources. But scientists figured that when you build systems around the model, and integrate them into the existing processes, it would level up the tasks.
Example - If you asked a single model to summarize a ‘customer support’ document, it will. But the moment you ask about a particular customer’s order status, it would fumble.
But, what if you could design a system to solve this problem? For that, you’d have to give the model access to a database where the customer data is stored. Now, the same query would go into the language model. The difference now is that the model would be prompted to create a search query, and that would be a search query that can go into the database that you have.
It would go and fetch the information from the database, give an answer, and then that would go back into the model that can generate a sentence to answer. This is an example of a compound AI system, and it recognizes that certain problems are better solved when you apply the principles of system design.
Retrieval augmented generation (RAG) model is one of the most popular and commonly used compound AI systems out there.
What is RAG? How Retrieval-Augmented Generation Enhances AI Agents
Large Language Models, inherently, have two major challenges:
- The response ‘generated’ by the model has no source to support its answer.
- And, the answer may be out of date.
Now, how do you solve this issue? Through RAG.
If you add a content store, like an open internet, to the LLM, the AI agents won’t output an answer from its training data. Instead, it will first go to that store and ask for an answer to the user's query.
tWhy? Because in RAG, the intelligent Agent is instructed to first retrieve information from the external data source, then, combine that with the user's question and only then generate the answer.
So, you now see the ‘retrieval-augmented’ part in RAG. You can augment your data store with new and updated information anytime, without needing to retrain the model-based agents. Since the model is instructed to rely on the primary source of data, it can give evidence for the output.
This makes it less likely to hallucinate because it is less likely to rely only on information that it learned during training.
What if the user's question cannot be reliably answered based on the data store? Then, the model should say, "I don't know," instead of making up something that is believable and may mislead the user.
This can have a negative effect as well. If the retriever is not sufficiently good to give the model the best, most high-quality information, then maybe the user's query that is answerable doesn't get an answer.
Researchers and engineers are working to improve the retriever to give the LLM the best quality data on which to ground its response. Along with that, they’re also working on the generative part so that the model can give the richest, best response to the user.
Suggested Read: Top 5 methods to Evaluate RAG models
Next Shift: From Compound AI systems to AI Agents
Imagine you want to bring in a very different query to the ‘customer support’ example. Let's ask about the weather in the model.
The intelligent agents are going to fail. Why? Because the path that this AI Agent has to follow is to always search the customer database. And that has nothing to do with the weather.
When we say the path to answer a query, we are talking about something called the control logic of a program. So, most of the compound AI systems have programmatic control logic, often executed by simple reflex agents or model-based reflex agents.
One other way of controlling the logic of a multi-agent system is to put an LLM in charge. This is only possible because we're seeing tremendous improvements in the capabilities of reasoning in large language models. This is where adavanced AI agents come in.
So, now you can feed LLMs complex tasks and you can prompt them to break them down. Another way to think about it is, on one end of the spectrum, you’re telling the system to “think fast, act as programmed”, and not deviate from the instructions given to you. And on the other end of the spectrum, you're designing your system to think slowly.
What are AI agents
AI agents go beyond simply responding to queries (prompts). They are intelligent agents designed to perceive their environment, plan actions, execute them, and learn from experiences.
They operate with a degree of autonomy, making decisions and adapting to changing circumstances. Advanced AI Agents can:
Plan: Break down complex tasks into manageable steps.
- Reason: Analyze information, identify gaps, and draw logical conclusions.
- Act: Execute actions, interact with systems, and adapt to changing circumstances.
- Learn: Refine their strategies based on feedback and experience.
A unique feature of an AI agent lies in its ability to have a large language model in charge of the control logic. This shift is made possible by the tremendous advancements in the reasoning capabilities of modern LLMs and natural language processing.
Instead of following a rigid, pre-programmed path, an intelligent agent leverages the LLM to understand specific tasks, break them down into smaller steps, and devise a dynamic plan to tackle them.
Think of it as moving from a simple reflex agent or model-based reflex agent that is told to "think fast, act as programmed, and not deviate" to one designed to a utility-based agent to "think slowly, create a plan, attack each part, identify roadblocks, and readjust the plan if necessary". This "thinking slowly" approach allows agents to handle much more intricate and varied tasks.
How Do AI Agents Work: The ReACT Framework
One popular method for configuring AI agents is the ReACT framework. It combines Reasoning and Acting.
In this approach, a user query is fed into an Large Language Model with instructions to think step-by-step and plan its actions. The AI agent can then decide to act by utilizing external tools to gather information or perform specific operations
After an action, the agent observes the result, determining if it brings it closer to the final answer or if the plan needs to be revised. This iterative process of reasoning, acting, and observing continues until a satisfactory solution is reached.
Let’s go back to our customer support example. Say, you put in this query - "I want to know a particular customer's order status and see what the weather would be on the day of delivery".
A compound AI system or advanced AI agent can break this into subtasks, retrieve data from the relevant sources, and provide an accurate, actionable response, demonstrating how multiple AI agents can coordinate to handle complex tasks.
When configured with the ReACT framework, the AI agent would follow an iterative process of Reasoning, Acting, and Observing. Here’s a breakdown of the query:
Reasoning: The LLM at the core of the ReACT agent would first analyze the user's request and break it down into distinct goals. It would identify the need to:
- Know the status of a specific customer's order. This requires identifying the customer and the order.
- Find out the delivery date for that order.
- Find the weather forecast for the location of the customer on the identified delivery date.
- Present the order status and the weather forecast to the user. The prompt given to the LLM would instruct it to think step-by-step and plan its work rather than providing an immediate answer.
Acting (through Tools): Based on the reasoned plan, the AI agent would then decide which external tools it needs to utilize to gather the necessary information. These tools could include:
- Customer Order Database Tool: This tool would allow the agent to query a database using customer identification. It helps to find the specific order and its current status, including the scheduled delivery date. The agent would generate a search query to retrieve this information.
- Weather API Tool: Once the delivery date and the customer's location are identified, the agent will use a weather API. It would call this API with the location and the delivery date to get the weather forecast for that day.
- Potentially a Clarification Tool: If the customer isn't identified in the initial query, the agent might use a tool to ask clarifying questions, such as "Could you please provide the customer's name or order number?"
Observing: After each action (tool call), the agent would observe the result.
- If the database tool successfully retrieves the order status and delivery date, the agent will observe this information. If the query to the database fails (e.g., no such order found with the given information), the agent would observe this error and might need to revise its plan. Perhaps by asking the user to double-check the information.
- If the weather API tool successfully returns the weather forecast for the specified location and date, the agent will observe this data. If the API call fails (e.g., due to an invalid location format), the agent would observe the error and might try to reformat the location or ask for clarification.
- If a clarification tool were used, the observation would be the user's response, which would then feed back into the reasoning and acting phases.
This iterative process of reasoning, acting, and observing would continue. For example, after getting the order status and delivery date, the agent would proceed to use the weather API.
Once both pieces of information are successfully retrieved, the agent would then formulate a final response to the user, such as, "The order for [Customer Name/Order Number] is currently [Order Status] and is scheduled for delivery on [Delivery Date]. The weather forecast for [Delivery Location] on [Delivery Date] is [Weather Forecast]".
If at any point an action doesn't yield the expected result, the agent would re-evaluate its plan based on the observation and try a different approach or tool if available. This shows the agent's ability to "think slowly," identify where it needs external help and adjust its strategy based on the outcomes of its actions.
Compound AI system or an AI agent: Which one to choose?
Agentic AI models provide a certain level of autonomy to an LLM. This autonomy is not always necessary or beneficial. For instance, if a problem is narrow and well-defined, with predictable query types, a more direct and programmatic compound AI system might be more efficient. In such cases, a fully agentic model could introduce unnecessary steps and loops, hindering efficiency.
However, when dealing with complex tasks that could involve a broad range of potential queries, the agentic approach becomes crucial. This is because the alternative – attempting to programmatically configure every possible query path with multiple AI agents, would be incredibly difficult, if not impossible.
Advanced AI agents, with their ability to adapt and respond to novel queries, are far better suited to navigate such complex and unpredictable problem spaces.
Therefore, the decision of whether or not to employ agentic capabilities or stick with a compound AI system should be based on a careful consideration of the task at hand. If the task is straightforward and the query types are predictable, a more programmatic approach might be preferable.
But if the task is complex and the query types are varied and unpredictable, an agentic model is likely the best tool for the job.
Types of AI Agents
AI agents come in different forms, each suited to specific tasks and levels of complexity. Here’s a quick breakdown:
- Simple Reflex Agents: Act purely on current inputs with pre-defined rules. Ideal for predictable, repetitive tasks where the environment doesn’t change much.
- Model-Based Reflex Agents: Maintain an internal model of the environment to make slightly more informed decisions. Useful when past states affect current decisions.
- Utility-Based Agents : Choose actions based on a measure of “utility” or value, optimizing outcomes across multiple possibilities. Great for tasks requiring trade-offs or prioritization.
- Human Agents Assisted AI: Collaborate with human agents, combining human intuition with computational speed. Perfect for tasks that require judgment and domain knowledge.
- Advanced AI Agents / Multi-Agent Systems: Multiple intelligent agents work together to tackle complex tasks, coordinating actions, reasoning, and planning. Ideal for large-scale operations or dynamic environments.
Understanding the type of AI agents that best fits your business need helps determine whether to deploy a single agent, multiple agents, or a combination of model-based and utility-based agents to maximize efficiency and insight.
Understanding AI Agents: Key Principles
AI agents are defined by a set of core characteristics that enable them to operate intelligently and autonomously in dynamic environments. These traits determine how they perceive, reason, learn, and collaborate to accomplish tasks, whether independently or alongside human agents and other AI agents. Key principles that shape AI agents include:
- Autonomy: AI agents operate independently, making decisions and executing tasks without constant human intervention. This is particularly valuable for complex tasks where multiple AI agents can coordinate in parallel, freeing human agents to focus on strategic work.
- Perception: Intelligent agents perceive their environment or input data through sensors, APIs, or connected systems. By integrating natural language processing, AI agents can interpret textual input, voice commands, or structured data, enabling them to act on relevant information effectively.
- Reasoning: AI agents reason by analyzing information, identifying patterns, and planning actions. Model-based agents or utility-based agents use structured approaches to determine optimal outcomes, while advanced AI agents can navigate uncertainty and prioritize tasks dynamically.
- Learning: Through experience or feedback, AI agents improve performance over time. Multi-agent systems allow multiple AI agents to share insights, enhancing overall system intelligence and ensuring that future tasks are handled more efficiently.
- Adaptability: AI agents can adjust to changing circumstances, unexpected inputs, or evolving objectives. This adaptability allows intelligent agents to tackle both specific tasks and broader, more complex tasks without requiring extensive reprogramming.
- Collaboration: AI agents are designed to work alongside other AI agents or human agents, coordinating efforts in multi-agent systems to solve problems more effectively. Collaboration enhances workflow efficiency, reduces errors, and allows AI systems to scale across multiple functions simultaneously.
Why Should Business Leaders Care About AI Agents
AI agents are a big step forward in how we use Artificial Intelligence. They're not just answering questions; they're using tools and learning from their experiences to solve more complex problems on their own. They’re not just answering questions; they’re using tools, learning from experiences, and collaborating with human agents to solve complex tasks independently.
Embracing AI agents is a total game-changer. In the fast-paced world of AI, businesses that jump on this opportunity will definitely have a leg up on the competition. Integrating AI agents into how you work can seriously optimize workflows, streamline decision-making, and spark innovation.
Staying ahead of the curve means not just exploring AI agent tech, but actually putting it to work. The potential benefits are huge: think improved efficiency, data-driven insights, and better customer experiences, you don't want to miss out on that.
Businesses that sleep on the transformative power of AI agents risk falling behind and missing out on major growth opportunities.
Zams has already helped sales leaders automate tasks and scale faster. Check out these success stories of how we have helped other leaders like you with AI adoption.
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FAQ's
What exactly are AI agents and how do they differ from traditional AI systems?
AI agents are intelligent agents designed to perceive their environment, plan actions, execute tasks, and learn from experiences. Unlike traditional monolithic AI systems, AI agents operate autonomously, adapt to changing circumstances, and can handle complex tasks. Advanced AI agents and multi-agent systems enable multiple AI agents to work together, making them ideal for businesses that require dynamic problem-solving and efficient workflows.
How do AI agents work in practice?
AI agents work by combining reasoning, acting, and observing in iterative cycles. Using frameworks like ReACT, AI agents can analyze queries, access external tools or data sources, execute actions, and adjust strategies based on results. Model-based agents, utility-based agents, or multiple agents in a system allow businesses to tackle complex tasks with precision and flexibility, all while collaborating seamlessly with human agents.
How can AI agents improve my businesses?
AI agents transform how businesses operate by automating repetitive tasks, providing real-time insights, and managing complex workflows. By integrating multiple AI agents, companies can handle intricate problems efficiently, reduce errors, and free human agents for high-value work. The result is faster decision-making, improved productivity, and enhanced customer experiences.
Why should my organization invest in AI agents now?
AI agents are no longer just a technical tool, they’re a strategic advantage. Businesses leveraging advanced AI agents and multi-agent systems can automate repetitive work, gain real-time insights, and handle complex tasks with fewer human resources. Integrating AI agents alongside human agents improves decision-making speed, increases operational efficiency, and positions your company ahead of competitors in an AI-driven landscape.
How do I know which type of AI agent is right for my business?
Choosing the right AI agent depends on the complexity and variability of your tasks. Simple reflex agents work well for predictable, repetitive processes, while model-based or utility-based agents are better for tasks that require reasoning, trade-offs, or prioritization. Advanced AI agents or multi-agent systems shine in dynamic environments where multiple AI agents must collaborate to handle complex workflows, integrate with human agents, and scale operations efficiently.
