Understanding the components of an AI agent: a five steps lifecycle

AI agents are the tech trend of the moment, promising to reshape industries, streamline operations, enhance customer experiences, and drive smarter decision-making in ways we couldn’t have imagined just a few years ago.

But how does an AI agent 'work' in practice?

As businesses, leaders, and innovators, it’s vital to understand how these systems actually function, not just from a theoretical standpoint but from a real-world perspective.?

In this blog, I’ll break down the core components?that make up an AI agent and explain how they come together to create the powerful, autonomous systems that everyone is talking about today.

(My hope is that, after reading this blog, you'll be able to explain it to your parents, kids, and friends who may have nothing to do with tech and AI as part of their daily lives and jobs.)

An AI agent typically consists of five main components:

1. Perception
2. Cognition
3. Decisioning
4. Action
5. Learning

Let's break down each component and use a?real-time fraud detection system?ale.

1. Perception: the eyes and ears of AI

At the heart of any AI agent is its ability to?perceive?the world around it. This is where the AI agent senses and collects information. Just like how we use our eyes to see and ears to hear, the AI agent gathers data from various sources (e.g., sensors, inputs, user interactions or databases) to understand the context in which it's operating.

To keep in mind in this phase:?the quality and breadth of data gathered during perception play a huge role in the AI’s overall effectiveness. If the AI lacks accurate or relevant data, its decisions will be based on incomplete information. Perception essentially sets the stage for everything that follows.

In the case of fraud detection, this is where the AI system collects data about every transaction that occurs in real time. This could include:

• The?transaction amount
• The?location?where the purchase is made
• The?time?of the transaction
• The?cardholder’s previous transaction history
• The?device or IP address?used for the transaction

For instance, when a customer uses their credit card to make a purchase, the system instantly captures this data and prepares it for analysis. In the context of fraud detection, it’s crucial to gather as much relevant data as possible to assess the legitimacy of each transaction.

2. Cognition: making sense of the data

Once the AI has gathered data through perception, it needs to?process?and?interpret?that information. This is where the?Cognition?phase comes into play. Here, the AI agent looks for patterns, identifies trends, and draws conclusions from the data it has collected. It can leverage a combination of analytics, machine learning, linguistic rules, inference, and LLMs.

To keep in mind in this phase: in this phase, the AI agent essentially “thinks” about the data - weighing different outcomes based on rules, probabilities or learned behavior. This gives the agent the foundation (cognitive understanding) to proceed to the next phase, where it will make a decision on what to do next. The more effectively it can process and understand the data, the better its decisions will be.

In our fraud detection system, the AI analyzes the transaction and compares it against historical data and known fraud patterns. Specifically:

• Pattern Recognition: The AI looks for discrepancies or unusual patterns. For example, if a credit card has never been used in another country before and suddenly a purchase is made from a foreign location, the system flags this as potentially suspicious.
• Risk Assessment: The system evaluates the risk of the transaction by considering various factors. For example, it might analyze whether the transaction amount is unusually high for that particular user, whether it’s a typical purchase for the cardholder, or if there are any signs of unusual behavior.

3. Decisioning: choosing the best path forward

The?Decisioning phase is where the agent determines the best course of action based on the insights gained during cognition. It’s like when we make a decision based on the information we have available, whether it’s choosing a business strategy, making a hiring decision, or reacting to a customer’s needs.

To keep in mind in this phase: Decisioning is a pivotal moment in an AI agent’s life. The decisions it makes drive its actions and ultimately determine its effectiveness. In business, a poor decision made by an AI agent could have financial, operational, or reputational consequences. Having a well-defined decision intelligence framework ensures that AI agents can make the right choices, even in complex environments.

I wrote about the importance of Decision Intelligence in the era of AI agents here.

In the fraud detection system, based on its analysis of the transaction and the perceived risks, the agent will make a decision. For example, after processing the data, it might have several options:

• Approve the transaction: If the transaction is deemed legitimate based on historical patterns, it will go through.
• Flag the transaction: If the AI is unsure, it might flag the transaction for further review by a human fraud analyst.
• Decline the transaction: If the AI identifies clear signs of fraud, such as a sudden purchase from an unknown location or a suspiciously high transaction amount, it may immediately block the transaction.

The decision-making process in agentic AI is not left to casualty. It should be based on a set of human pre-determined rules that help the system assess the best decision. That is why LLMs alone are not sufficient to build AI agents. You need a decisioning framework that combines LLMs, busienss rules, analytics, ML, AI governance.

4. Action: implementing the decision

Once the agents make a decision, they take action. This is the phase where the system executes the chosen course of action. This could be performing a task, making a recommendation, or triggering a response in another system or agent.

In the fraud detection agent, the actions could be:

• Immediate Response: If the transaction is flagged as fraudulent or suspicious, the AI agent could block the transaction, alert the cardholder, or notify the fraud detection team for manual review.
• User Notification: If a transaction is declined or flagged for review, the cardholder might receive a real-time notification asking them to verify the transaction.
• Example: In the case of the flagged international purchase, the system might decline the transaction, send an alert to the cardholder, and ask for confirmation through their phone or email.

To keep in mind in this phase: Action is the phase where the AI agent delivers its value. It’s no longer just thinking or analyzing - it’s doing. For AI agents to be useful in business, its actions must align with strategic goals.

5. Learning: continuously improving over time

The final component of an AI agent is?Learning. Unlike traditional systems that require manual updates or adjustments, AI agents can learn and improve over time by analyzing the outcomes of their actions.

After the agent takes action, it assesses the results. Did the action lead to the expected outcome?

• If the action was successful, the AI agent strengthens the model and continues making similar decisions in the future.
• If it was a failure, the agent adjusts its models to improve. For instance, if a fraud detection system incorrectly flagged a legitimate transaction, it will learn from that mistake to avoid similar errors in the future.

To keep in mind in this phase: Learning is what sets AI agents apart from traditional software. It enables AI to adapt, evolve, and get better with each interaction. Over time, they become more accurate, more efficient, and more aligned with business goals.

The role of the environment

While we’ve covered the five key components of an AI agent, it’s also important to recognize the importance of the?environment?in which the AI operates.

This refers to everything the AI agent interacts with, such as systems, people, or the processes it is designed to manage. The environment provides the context and feedback for the agent's perception, cognition and actions, directly affecting the quality of its decisions and its ability to learn and improve in the Learning phase.

• External data sources?(third-party fraud databases, news about data breaches, etc.)
• Regulatory frameworks?(legal requirements for handling sensitive customer data)
• Customer behavior and feedback, which can inform future decisions and adjustments
• Market conditions, which may impact fraud risk at different times (a rise in online shopping during the holiday season)

The AI system relies on data from its environment to stay informed about potential fraud risks, and this external context helps refine its decision-making.

Example of environment in action in the fraud detection scenario

Imagine a scenario where a fraud detection AI agent detects a large transaction being made in a location far from where the cardholder usually shops. The environment impacts the process through:

• External data source: The agent might check with the bank's transaction history to see if this behavior fits the cardholder's usual spending patterns. It may also consider publicly available data, such as news reports or records of recent data breaches in the region or industry, which could indicate increased fraud risk in certain locations or times.
• Contextual factors: The agent might notice that there has been a recent data breach in the region and that many cards have been compromised, increasing the likelihood that this transaction is fraudulent.
• Human interaction: The customer receives an alert, and they confirm that it was not them making the purchase. The environment in this case (the human feedback) leads the system to flag the transaction as fraudulent.
• Regulatory compliance: Compliance?with financial regulations (such as GDPR, PCI-DSS, etc.) is part of the environment. The fraud detection system must be aware of legal constraints when it collects, processes, and stores sensitive data. For example, it may have to handle user data in specific ways depending on the country of origin or the financial regulations in place. The system might also ensure that its actions comply with data privacy laws, notifying the customer and handling their data securely.

AI agents aren’t just about automation or LLMs; they represent an adaptive, intelligent approach to solving problems and making better decisions faster.