LLMs Going Beyond the Chat Window: AI Agent Examples

More often than not, articles on AI trends are just noise. The same goes for discussions about AI’s supposed growing consciousness. But now and then, AI tech comes out with a bang, forever changing how we do things. LLMs and ChatGPT are prime examples. The next technology poised to do the same is agentic AI – also called AI agents, intelligent agents, or whatever name you’ve heard for them. They are the next stage in AI’s evolution. They don’t just sound good on paper but have massive, real-world utility. They’re the first predictive tools capable of genuine reasoning, able to handle a complex task on their own. 

This post explores why AI agents will dominate the tech landscape in 2025. It also explains the shift in the AI space that brought them about. 

Generative AI Shifts That Preceded AI Agents 

Monolithic models have been giving way to compound AI systems. Why? Because the former – no matter how advanced (think ChatGPT) – are limited by their training data. This data defines what the model “knows” and what it can do. 

Adapting these algorithms to your needs is a colossal challenge. While you can tune them with specific data, it’s costly and takes way too much time and resources. If you try explaining new contexts or familiarizing the AI with relevant details for each task –  you’ll get shoddy results most of the time. The model always defaults to its training and will frequently merge its core knowledge with new information, resulting in odd, inaccurate responses. Worse still, it won’t remember your explanations the next time you ask. It’s simply impractical. 

Take this example: You want to bet on your favorite team but aren’t sure how much to stake. If you just ask a bot like Gemini for advice, its answer will be off—it doesn’t know you or how much you can afford to lose. 

So, what can we do? 

Well, we can add some good old programming to the mix. Let’s say you give the LLM access to a database with your balance details and instruct it to create a search query. Next time, after receiving the prompt, it will fetch the needed data, process it, and provide a useful, actionable answer. 

This approach can be called using a system design. It aims to combine the powers of different elements – a modular architecture – to achieve the desired output. In our case, the modules are a pre-trained LLM with a database search as an auxiliary tool. This method is faster, cheaper, and more efficient than fine-tuning.  

What tools can be used in such a compound system use besides an LLM? 

• Other LLMs (fine-tuned or not) 
• image generation models 
• programmatic elements like output verifiers 
• database searches 
• APIs 
• simple calculators 

Or, basically, anything else. Whatever the issue calls for can be added to assist the LLM. 

But there’s a catch. 

If we prompt our LLM-search system with a different task – like asking whether it’s a good idea to bet on the team at all – it will return nonsense. This AI follows a pre-defined path. Its control logic can’t deviate from the rules we set. So, it will query the database again, won’t find relevant data this time, and, as a result, produce a useless response. 

These are the limitations of programming the control logic in compound systems. The system can’t help but adhere to rigid instructions. It will act fast but will only be usable in narrow contexts. 

What Is an AI Agent? 

AI agents change the game by introducing varying degrees of AI reasoning to control logic. We know that when we ask ChatGPT a complex question, it breaks down the answer into steps. It creates a plan. This exact capability is what we want to harness in agentic AI – we want to leverage an LLM’s analytical powers and put it in charge of the process orchestration. 

To reiterate: unlike programmed systems that simply follow instructions, this type of agent evaluates each problem to determine what needs to be done. It charts a path to resolution, handles each step or subtask independently, and calls on external tools when necessary. AI becomes both the brain and the executor of the entire process. 

How Does an AI Agent Work and What Makes It Unique? 

Let’s revisit the betting example. Suppose we ask an advanced AI agent: “Should I bet on Real Madrid’s next match given how they’ve been playing, and if so, how much?” This time, the system won’t provide a surface answer. Instead, it will plan its steps (reasoning), use tools to fetch and analyze data (action), and consider past interactions and expressed preferences (memory). Only then will it return a response. 

To make it concrete, imagine this sequence of actions taking place after your prompt: 

1. The LLM concludes it needs to Google whether Real Madrid has been on a winning streak and review analysts’ predictions for the upcoming match. 
2. Next, it determines it needs to know your balance. It queries an appropriate database and then uses a calculator to figure out the percentage you can afford to lose. 
3. Finally, it references your previous conversations to see how much you’ve bet in the past and delivers a reply with a reasonable suggestion aligned with your habits. 

This is a simplistic example, but it clearly shows how these modular systems, unlike monolithic AI models, can be highly versatile and helpful across a wide range of tasks. 

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What Do We Mean When We Say a Type of AI Agent? 

Now that we understand the principles, let’s explore the types of agents that exist. These systems vary in their degree of autonomy, the amount of knowledge they utilize, their decision-making paradigms, and how strictly they adhere to pre-programmed rules. Let’s go from simple to complex. 

Simple Reflex Agent 

These are the most basic forms of AI agents. Their decisions are informed only by the current perception. They have no memory and don’t consider the future. They perceive their environment and react to it – on reflex, so to speak – without creating a model of the world or interacting with other systems. 

For them to be effective, the current state of the environment must provide all the information needed to complete the task. Otherwise, they’ll fail. Unfamiliar situations leave them helpless. 

How they work: 

• Gather information about the current moment. 
• Compare it against pre-programmed criteria. 
• If a rule fits, act. 

These agents are the easiest to configure and implement. They’re also the fastest. However, since they can’t learn or adapt, they can’t do much more than power simple systems like thermostats, traffic lights, etc. 

Model-Based Reflex Agent 

These systems can preserve memories and improve over time. They create a model of the world (hence the name) and update it as new data comes in. While they can perform reasonably well in partially observable and changing environments, they’re still limited by their adherence to predefined rules. 

How they work: 

• Collect info about the current state of the environment. 
• Update the internal model based on perceived information and the effects of previous actions. 
• Use the internal model to simulate potential actions and predict their likely outcomes. 
• Select the action most likely to align with the pre-programmed objective. 

Model-oriented agents are more sophisticated than the first type, but they also require more computational resources. 

Goal-Based Agent 

In addition to maintaining a model of the world, these agents are driven by specific goals. They create plans and search for sequences of actions that bring them closer to achieving those goals. 

How they work: 

• Explore action sequences that can lead to a predefined goal. 
• Evaluate each path based on its likelihood of achieving the desired goal. 
• Choose the sequence most likely to succeed. 

The computational requirements for these agents depend heavily on the complexity of the search space and the chosen search algorithm. They can be modest or extremely compute hungry. 

Utility-Based Agent 

Utility-oriented agents not only aim to achieve goals but also maximize the utility of their actions. This means they evaluate sequences of actions based on additional criteria like speed, cost-efficiency, or risk reduction. These criteria are, of course, defined by humans beforehand. 

How they work: 

• Based on a utility function, assign numerical values to different states or outcomes, where higher values represent more desirable results. 
• Select actions expected to maximize utility. 
• Employ optimization techniques to learn the best actions for maximizing utility over time.

Learning Agent 

These are the most advanced. Learning agents combine the capabilities of all the previous types with a sophisticated mechanism for learning. They start with a foundational understanding of the world and autonomously gather new data to expand their knowledge base. This enables them to improve their effectiveness in unfamiliar environments over time. 

What sets learning agents apart is that they’re comprised of four specialized components, each playing a unique role: 

• Learning. Accumulate knowledge by interacting with the environment. 
• Critic. Use predefined performance metrics to evaluate actions and provide feedback. 
• Performance. Select actions based on what’s been learned. 
• Problem Generator. Propose new sequences of actions, enabling exploration beyond standard behaviors. 

Unlike lower-level agents, learning agents’ ability to adapt and improve makes them the most versatile and effective in complex, dynamic, and unobservable environments. 

Real-World Examples of AI Agents’ Capabilities 

E-commerce 

AI agents can track customer browsing, purchases, and social media to learn preferences. Then, they’ll suggest products, personalize offers, optimize carts, and find discounts and complementary items.  

Logistics 

In logistics, companies can use AI agents to optimize routes, predict delivery times, and address disruptions like traffic or weather delays.  

iGaming 

In iGaming, AI agents offer personalized betting recommendations, quick onboarding, 24/7 support, and much more. They can suggest games, help adjust betting limits, and provide tailored predictions. Additionally, they help casino and sportsbook providers significantly simplify tasks like account setup and bet placement for their players. 

Healthcare 

In short, AI agents can make healthcare more accessible. They deliver information and perform services – drug recommendations, symptom analysis, lifestyle guidance, and more. They understand patient needs deeply. For customer service, the agent handles routine tasks like appointment scheduling with speed. 

Airline 

In the airline industry, AI can give real-time flight updates, manage frequent flyer programs, help passengers with travel documentation, and take many customer inquiries off carriers’ support staff. 

As you can see, they can be useful across numerous industries and real-world scenarios. For more detailed examples, explore Harmony, our personalized assistant for healthcare, retail, airline, supply chain, finance, analytics, benchmarking, and BetHarmony—a tailored AI solution with integrated casino, sportsbook, and 24/7 customer support features, specifically for iGaming. 

How Do We Build an AI Agent? 

Building and training AI agents is tough. As we said, the tools included can range from deep learning models to something like a calculator. So, here at Symphony Solutions, the process always starts with defining the agent’s purpose and scope (use cases, tasks, target audience, etc.) 

Data is key. The agent’s effectiveness depends on the quality of the data used to train it. This is true for any AI model. Therefore, we must gather everything -text, voice recordings, interaction logs, and so on – and ensure they are clean and well-structured. Data cleansing, transformation, and augmentation usually play a big role, too. 

Though less exciting than AI training, data preparation largely determines the project’s success. 

During training, we optimize the model’s parameters. We adjust them until the agent’s performance satisfies us. The model undergoes unit, user, and A/B testing. If we discover that the AI is only prepared for scenarios seen in training and too brittle for the unforeseen – this is called overfitting – we may need to revisit the training. Through thorough testing and validation, we get the AI agent to the desired level of accuracy. 

Finally, we deploy the agent on platforms of the client’s choice – websites, apps, or voice systems – and monitor performance. We may use tools that provide real-time insights into response times, success rates, and user satisfaction to get a clearer picture. 

Our proven approach ensures the agent does well from the outset and stays effective (and improves) over time. 

Wrapping Everything Up  

An agent is a program that combines artificial intelligence with other tools to perform a task. Its interface makes communication simple and clear. It operates with a degree of autonomy, deciding which tools to use and when, instead of relying on fixed human-set rules. This has vast implications across industries. The value of agentic AI for enterprises is clear and undeniable, something that’s rare in AI research. 

Symphony Solutions offers full AI agent development services – training, engineering, monitoring, and refinement. If you want to harness the power of agentic solutions in your workflows, contact us here. Let’s start propelling your business to new heights today.