AI agents are basically smart software programs that can understand what you need, make decisions on their own, and actually take actions to get things done without you having to babysit them.
An AI agent is basically a smart digital assistant that figures out how to get stuff done on its own by putting together different tools and creating step-by-step workflows.
These agents aren't just fancy chatbots - they're more like digital problem-solvers that can make decisions, tackle complex challenges, work with different systems, and actually take real actions to complete tasks.
You'll find AI agents handling all sorts of business-critical work: designing software, automating IT processes, writing code, and helping with customer conversations. They tap into the power of large language models to really understand what you're asking for, break it down into manageable steps, and know exactly which tools to grab from their digital toolbox to get the job done.
AI agents are basically souped-up versions of large language models (LLMs) - which is why you'll sometimes hear them called "LLM agents." But here's where things get interesting: regular LLMs like IBM's Granite models can only work with whatever they learned during training, so they're stuck with outdated info and can hit walls pretty quickly.
AI agents break free from these limitations by actually reaching out and using real tools. They can grab fresh information from the internet, create their own workflows, and break big complicated tasks into smaller, manageable pieces - all without you having to hold their hand through every step.
The really cool part? These agents get smarter about working with you over time. They remember your past conversations and use that knowledge to plan better responses for the future. It's like having a digital assistant that actually learns your preferences and gets better at anticipating what you need.
All of this tool-calling and decision-making happens completely automatically - no human babysitting required. This is what opens up so many real-world possibilities for these AI systems. The whole process breaks down into three main stages that define how these agents actually operate:
Even though AI agents can make their own decisions, they still need humans to point them in the right direction and set some ground rules. Three different groups of people shape how these agents actually behave:
Once the agent knows what you want and what it has to work with, it goes into task-breaking mode. Basically, it looks at your big, complicated goal and figures out how to chop it up into smaller, more manageable pieces that it can actually tackle one by one.
Now, if you're asking it to do something pretty straightforward, the agent might skip all this planning stuff entirely. Instead, it'll just try something, see how it goes, and adjust on the fly without needing to map out every single step ahead of time.
Making Smart Decisions About Which Tools to Grab
AI agents are pretty smart, but they definitely don't know everything. When they hit a knowledge wall trying to complete part of a task, they do what any smart person would do - they go find the right tools and resources to fill in the gaps.
This might mean digging into external databases, running web searches, connecting to APIs, or even chatting with other specialized AI agents. Once they gather the missing pieces, they update their understanding and constantly rethink their approach, making course corrections as they learn new information.
Here's a perfect example: let's say you ask an AI agent to figure out the best week next year for a surfing trip to Greece. The agent's core language model isn't exactly a weather expert, so it can't just wing it with its built-in knowledge.
First move? The agent taps into external weather databases with years of daily reports for Greece. But even with all that weather data, it still doesn't know what makes for good surfing conditions.
So it creates a new mini-task: connect with a surfing-specialized AI agent to learn the basics. Through this conversation, it discovers that surfers need high tides, sunny skies, and minimal rain for the best conditions.
Now comes the magic - the agent combines everything it's learned from different sources to spot patterns in the data. It can predict which week next year will likely hit that sweet spot of high tides, sunshine, and dry weather in Greece, then present you with its findings.
This ability to mix and match information from different tools is what makes AI agents so much more versatile than traditional AI models that can only work with what they already know.
AI agents use feedback mechanisms, such as other AI agents and human-in-the-loop (HITL) to improve the accuracy of their responses. Let’s return to our previous surfing example to highlight this process. After the agent forms its response to the user, it stores the learned information along with the user’s feedback to improve performance and adjust to user preferences for future goals.
If other agents were used to reach the goal, their feedback might also be used. Multiagent feedback can be especially useful in minimizing the time that human users spend providing direction. However, users can also provide feedback throughout the agent's actions and internal reasoning to better align the results with the intended goal.2
Feedback mechanisms improve the AI agent's reasoning and accuracy, which is commonly referred to as iterative refinement.3 To avoid repeating the same mistakes, AI agents can also store data about solutions to previous obstacles in a knowledge base.
AI chatbots use natural language processing to understand what you're asking and spit back automated responses. But here's the thing - chatbots are just one way AI can talk to you, while being "agentic" is a whole different level of smart technology.
Regular chatbots without the agent superpowers are pretty basic. They don't have access to tools, can't remember previous conversations, and can't really think things through. They're only good for quick, simple tasks and need you to keep feeding them information to get anything done.
Sure, they can handle the usual questions pretty well - the kind of stuff they've seen a million times before. But ask them something unique about your specific situation or data? They'll probably face-plant. And since they have zero memory, they can't learn from their mistakes when they give you a terrible answer.
Agentic AI chatbots are a completely different animal. They actually get better at helping you over time by learning your preferences and giving you more tailored, thorough responses. They can tackle complicated requests by breaking them down into smaller tasks, all without you having to micromanage every step.
The real game-changer? These smart chatbots can course-correct themselves and update their approach as they go. Plus, they know how to use their available tools and resources to fill in knowledge gaps, instead of just shrugging and giving you a generic "I don't know" response.
There is not one standard architecture for building AI agents. Several paradigms exist for solving multistep problems.
The ReAct approach is pretty clever - it basically teaches AI agents to "think out loud" after every action they take and every response they get from their tools. This creates these ongoing Think-Act-Observe cycles where the agent is constantly figuring out what to do next, step by step.
Here's what makes it cool: through the way you structure the prompts, you can get agents to slow down and actually show you their reasoning process. Instead of just giving you an answer out of nowhere, the agent walks you through its "thought process" - like having a really smart colleague explain their work as they go.
This transparency is huge because you can actually see how the agent arrived at its response, rather than just getting some mysterious black-box answer. The agent keeps building on its understanding with each new piece of reasoning, constantly updating what it knows.
If this sounds familiar, it's because it's basically a more advanced version of Chain-of-Thought prompting - that technique where you get AI to break down complex problems by thinking through them step by step.
ReWOO takes a totally different approach from ReAct - instead of making decisions on the fly, the agent maps out its entire game plan right from the start. As soon as you give it a prompt, it figures out exactly which tools it'll need and in what order, rather than stumbling around and potentially using the same tool multiple times.
This upfront planning is actually pretty user-friendly because you get to see the agent's entire strategy before it starts doing anything. You can basically say "yeah, that looks good" or "hold up, try a different approach" before it goes off and executes the plan.
The whole ReWOO process breaks down into three simple parts: First, the agent creates its master plan based on what you asked for. Next, it goes out and actually uses all the tools it identified in step one to gather the information it needs. Finally, it takes that original plan and combines it with all the tool outputs to give you a complete answer.
The beauty of this approach? It's way more efficient - less computational overhead, fewer tokens used, and if one of the tools crashes halfway through, the agent doesn't have to completely start over since it already knows the big picture plan.
AI agents can be developed to have varying levels of capabilities. A simple agent might be preferred for straightforward goals to limit unnecessary computational complexity. In order of simplest to most advanced, there are 5 main agent types.
Simple reflex agents are basically the "if this, then that" robots of the AI world. They don't remember anything from previous interactions, can't ask other agents for help when they're stuck, and they definitely don't get creative with problem-solving.
These agents run on a bunch of pre-programmed rules or "reflexes" - kind of like having a really rigid instruction manual that they follow to the letter. When a specific condition is met, they automatically perform the corresponding action, every single time.
Here's the catch: if they run into a situation that's not in their rulebook, they completely freeze up or do something totally wrong. They're basically useless when facing anything unexpected or new.
But in the right environment - one that's predictable and where you can anticipate pretty much everything that might happen - these simple agents can actually work quite well.
Perfect example: Think of a basic programmable thermostat. If the time hits 8 PM, turn on the heating system. That's it. No thinking required, no adaptation needed - just a simple rule that gets the job done night after night.
Smarter Agents That Actually Remember Things
Model-based reflex agents are like the upgraded version of those basic rule-followers - they still follow pre-programmed instructions, but now they've got memory and can actually keep track of what's happening around them.
These agents build and constantly update their own mental map of the world based on what they're experiencing right now plus everything they remember from before. So their decisions aren't just based on rigid rules - they also consider their internal model, past experiences, and current situation.
This memory upgrade is huge because now they can work in messy, unpredictable environments where they can't see everything at once and things keep changing. They're not completely lost when something unexpected happens.
But here's the thing - they're still basically rule-followers at heart. They might be smarter about it, but they're still limited by their pre-programmed instructions.
Think robot vacuum cleaner: As it's buzzing around your living room, it senses furniture and walls, then adjusts its path to work around them. But the clever part? It remembers which areas it's already cleaned so it doesn't just keep going in circles, endlessly vacuuming the same spot over and over. It's building a mental map of your room and using that memory to clean efficiently.
Goal-based agents take things up another notch - they've got that internal world model plus actual goals they're trying to achieve. Instead of just reacting to whatever's happening around them, these agents are actively hunting for the best sequence of actions that'll get them where they want to go, and they think it all through before making a move.
This planning-ahead approach makes them way more effective than those simpler agents that just follow rules or react to immediate situations. These goal-based agents are actually strategizing.
Perfect example: Your GPS navigation system. It doesn't just know where roads are - it has a clear goal (get you to your destination) and actively searches through different possible routes to find the best one. The smart part? If it discovers a faster route while you're driving, it switches recommendations because its core rule is "always suggest the quickest path to the goal."
It's not just blindly following "turn left at Main Street" instructions - it's constantly evaluating different options against your actual objective and adjusting its plan accordingly.
Utility-based agents are the perfectionists of the AI world - they don't just want to reach their goal, they want to reach it in the absolute best way possible. These agents look at all the different action sequences that could get them there, then pick the one that gives them the highest "score" based on what they value most.
Here's how they decide: They use something called a utility function, which is basically their internal scoring system. This function looks at each possible scenario and assigns it a value based on a bunch of different criteria - like how quickly it gets to the goal, how much effort it takes, or how much it costs.
The agent then does the math and picks whatever option gives it the highest utility score. This makes them super useful when you've got multiple ways to accomplish the same thing but need to find the optimal approach.
Think upgraded GPS system: Instead of just finding any route to your destination, this smarter navigation system optimizes for fuel efficiency, avoids traffic jams, and minimizes toll costs all at the same time. It's weighing all these different factors against each other and picking the route that gives you the best overall value - not just the fastest, but the best combination of speed, cost, and efficiency.
It's like having a really smart friend who doesn't just help you get there, but figures out the smartest way to get there.
Learning agents are basically all the other agent types plus a superpower - they can actually get better at their jobs over time. These agents automatically add new experiences to their knowledge bank, which helps them handle situations they've never seen before. They might be goal-focused or utility-maximizing in how they think, but what makes them special is this learning ability.
Here's how they're built with four key parts:
Perfect real-world example: Those product recommendations you see on Amazon or Netflix. These systems watch everything you do - what you buy, what you browse, what you ignore - and store all that activity in their memory. Each time they suggest something new, they're learning from whether you actually clicked on it or bought it. The whole cycle keeps repeating, and the agent gets better and better at predicting what you'll actually want instead of just throwing random suggestions at you.
With the ongoing advancements in generative AI and machine learning, there is a growing interest in workflow optimization through AI, or intelligent automation. AI agents are AI tools that can automate complex tasks that would otherwise require human resources. This shift translates to goals being reached inexpensively, rapidly and at scale. In turn, these advancements mean human agents do not need to provide direction to the AI assistant for creating and navigating its tasks.
Multiagent frameworks tend to outperform singular agents.11 This is because the more plans of action are available to an agent, the more learning and reflection occur.
An AI agent incorporating knowledge and feedback from other AI agents specializing in related areas can be useful for information synthesis. This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence.
AI agents provide responses that are more comprehensive, accurate and personalized to the user than traditional AI models. This adaptability is important to us as users because higher-quality responses typically yield a better customer experience. As previously described, this capability is made possible through exchanging information with other agents, through tools and updating their memory stream. These behaviors emerge on their own and are not preprogrammed.12
Certain complex tasks require the knowledge of multiple AI agents. Orchestration of these multiagent frameworks has a risk of malfunction. Multiagent systems built on the same foundation models might experience shared pitfalls. Such weaknesses can cause a system-wide failure of all involved agents or expose vulnerability to adverse attacks.13 This highlights the importance of data governance in building foundation models and thorough training and testing processes.
The convenience of the hands-off reasoning for human users enabled by AI agents also comes with its risks. Agents that are unable to create a comprehensive plan or reflect on their findings, might find themselves repeatedly calling the same tools, causing infinite feedback loops. To avoid these redundancies, some level of real-time human monitoring might be used.13
Building AI agents from scratch is both time-consuming and can also be computationally expensive. The resources required for training a high-performance agent can be extensive. In addition, depending on the complexity of the task, agents can take several days to complete tasks.12
If mismanaged, the integration of AI agents with business processes and customer management systems can raise some serious security concerns. For example, imagine AI agents leading the software development process, taking coding copilots to the next level, or determining pricing for clients—without any human oversight or guardrails. The results of such scenarios might be detrimental due to the experimental and often unpredictable behavior of agentic AI.
Therefore, it is essential for AI providers such as IBM, Microsoft and OpenAI to remain proactive. They must implement extensive security protocols to ensure that sensitive employee and customer data are securely stored. Responsible deployment practices are key to minimizing risk and maintaining trust in these rapidly evolving technologies.
To address the concerns of multiagent dependencies, developers can provide users with access to a log of agent actions.14 The actions can include the use of external tools and describe the external agents used to reach the goal. This transparency grants users insight into the iterative decision-making process, provides the opportunity to discover errors and builds trust.
You definitely don't want AI agents running wild forever - especially when they get stuck in endless loops, lose access to their tools, or start malfunctioning because of some bug in their design. Nobody wants their AI agent to turn into that friend who won't stop talking at a party.
The solution is pretty straightforward: build in an emergency brake. This means giving human users the power to step in and gracefully shut things down when the agent is going off the rails or just taking way too long to complete a task.
But here's where it gets tricky - you need to be smart about when you actually pull the plug. Sometimes hitting the stop button can actually make things worse.
Think about it this way: if you've got a buggy AI agent that's trying to help during a medical emergency, it might be safer to let the flawed agent keep helping rather than completely cutting off the assistance. Sure, it's not perfect, but something is better than nothing when lives are on the line.
The key is giving humans the choice - they can assess the situation and decide whether the agent's imperfect help is still better than no help at all
To mitigate the risk of agentic systems being used for malicious purposes, unique identifiers can be implemented. If these identifiers were required for agents to access external systems, tracing the origin of the agent’s developers, deployers and user would become easier.
This approach adds an essential layer of accountability. Traceability helps identify responsible parties when an agent causes malicious use or unintended harm. Ultimately, this kind of safeguard would foster a safer operational environment for AI agents.
To assist in the learning process for AI agents, especially in their early stages in a new environment, it can be helpful to provide some level of human oversight. So, based on this guidance, the AI agent can compare its performance to the expected standard and make adjustments. This form of feedback is helpful in improving the agent’s adaptability to user preferences.5
Apart from this safeguard, it is best practice to require human approval before an AI agent takes highly impactful actions. For instance, actions ranging from sending mass emails to financial trading should require human confirmation.7 Some level of human monitoring is recommended for such high-risk domains.
