AI Agents: Separating Fact from Fiction

Stephanie Gradwell Managing Partner

9 min read .

If you’ve spent time on the internet lately, you’d think AI agents were a brand-new phenomenon poised to revolutionise every aspect of business overnight. The reality? AI agents have been widely used in industries since 2016, working quietly in the background, making critical decisions, and delivering tangible value.

So, what exactly are AI agents? How do they work? And why is so much of the current discourse misleading? Let’s cut through the noise and explore the practical application of AI agents, using real-world examples that have been shaping industries long before the current hype cycle.

What are AI Agents?

AI agents are autonomous systems designed to perceive their environment, process information, make decisions, and execute actions to achieve specific objectives. Unlike traditional software that requires explicit programming for each task, AI agents adapt, learn, and optimise over time.

One of the most recognisable AI agents today? Credit card fraud detection systems.

Remember the last time your bank contacted you about an unusual transaction. You may have booked a hotel abroad, and within seconds, your bank flagged the purchase, asking you to confirm or deny it. That wasn’t a human making a real-time judgment—it was an AI agent, designed to assess risk, decide, and act.

While fraud detection automation has existed for decades, it wasn’t until 2016 that these systems evolved into AI agents, capable of adapting and learning in real time.

Let’s break this down further.

Types of AI Agents

AI agents come in different forms, depending on their design and function. While they all follow the principles of perception, reasoning, decision-making, execution, and learning, they vary in complexity:

1. Simple Reactive Systems (Not True AI Agents)

  • These systems respond purely to immediate stimuli based on predefined rules without memory, learning, or reasoning. While they are often called agents, they do not meet the full definition of AI agents. An example is rule-based chatbots that answer simple customer queries using static scripts.

2. Deliberative (Goal-Oriented) Agents

  • These agents plan and reason before acting, optimising for long-term outcomes rather than immediate reactions. An example is AI-driven route optimisation systems in logistics that analyse traffic, weather, and delivery schedules to find the most efficient route.

3. Learning Agents

  • These agents use machine learning to improve performance by adapting to new data and feedback loops. Example: AI-powered recommendation engines, such as those used by Netflix or Spotify, refine suggestions based on user preferences and engagement.

4. Hybrid Agents

  • A combination of reactive and deliberative agents, allowing them to react instantly while planning ahead. Example: AI cybersecurity systems that block immediate threats while also analysing long-term attack patterns to predict and prevent future breaches.

5. Multi-Agent Systems (MAS)

  • These involve multiple AI agents working together—either collaboratively or competitively—to achieve a shared objective. An example is AI-powered financial trading systems, where agents analyse market trends and execute trades based on diverse strategies.

How AI Agents Work

Now that we have discussed what an AI Agent is and the types of AI Agents, let’s examine an everyday use case of a Learning AI Agent—Credit Card Fraud Detection.

A fraud detection AI agent follows five key stages:

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Simple Top level Learning Agent Architecture Diagram

1. Data Input: Collecting Signals in Real Time

Before making any decision, an AI agent requires data. Fraud detection systems aggregate vast amounts of structured and unstructured data, such as:

  • Transaction Details – Amount, merchant, time, location
  • User Location – GPS tracking, device IP, past travel patterns
  • Spending History – Normal vs. abnormal spending behaviour
  • Known Fraud Patterns – Flagged accounts, compromised card numbers

At this stage, data pre-processing models may be used to clean and normalise incoming information, ensuring it’s usable by downstream AI models.

2. Processing & Reasoning: Detecting Potential Fraud

Once the data is collected, it moves into analysis. Multiple machine learning models work independently before feeding into the decision engine:

  • Anomaly Detection (Isolation Forests, Autoencoders) – Identifies transactions that deviate from the user’s typical spending patterns. Example: A customer usually spends £50 on groceries but suddenly spends £5,000 on electronics in a different country.
  • Fraud Network Mapping (Graph Neural Networks) – Determines if the transaction is linked to known fraud rings or compromised accounts. Example: The merchant has been associated with previous fraud cases.
  • Predictive Models (XGBoost, Random Forest, Logistic Regression) – Assigns a fraud probability score based on past transactions. Example: A transaction receives a 90% fraud risk score if it shares characteristics with known fraudulent activity.

Each model contributes to an overall fraud risk score, but no single model makes the final call. Instead, their outputs are aggregated before moving to the next stage.

3. Decision Engine: Weighing the Evidence and Acting

At this stage, the AI agent does not block or allow a transaction at random—it follows a structured decision-making process.

  • Weighted Scoring Models – Combine multiple fraud indicators into a single confidence score. Example: If anomaly detection indicates a 70% risk and fraud network mapping indicates an 80% risk, the weighted scoring model might calculate an overall 75% risk score.
  • Bayesian Networks – Assess the probability of fraud based on historical data and real-time inputs. Example: If similar transactions have resulted in fraud 60% of the time, the Bayesian model incorporates this probability into the decision.
  • Reinforcement Learning – Optimizes fraud detection over time, reducing false positives.

If the fraud risk score exceeds a set threshold, the decision engine initiates an action:

  • High confidence of fraud? Transaction is blocked immediately.
  • Medium confidence? AI triggers a user confirmation request.
  • Low confidence? Transaction is approved but flagged for monitoring.

At this point, the AI agent decides without human intervention, based purely on statistical confidence and past learning.

4. Execution Layer: Acting in Real Time

Once the decision is made, the system takes immediate action:

  • Transaction Approved – Proceeds as usual.
  • Transaction Declined – The user is notified, and the card may be temporarily frozen.
  • User Confirmation Requested – The bank sends a text, push notification, or call asking: “Did you authorise this transaction?”

If the user confirms the transaction was legitimate, the AI learns from this feedback to improve future fraud detection accuracy.

5. Feedback Loop: Continuous Learning & Adaptation

An AI agent is only as good as its ability to learn from mistakes. The feedback loop ensures that each decision improves the next one.

  • Federated Learning – AI models update without storing sensitive customer data.
  • Adaptive Risk Scoring – If too many false positives occur, fraud thresholds are adjusted dynamically.
  • User Confirmation Signals – Helps fine-tune fraud detection, balancing security and user experience.

This constant feedback cycle allows fraud detection AI agents to evolve in real time, improving their accuracy with each transaction.

Why AI Agents Don’t Work Without Humans

Despite the hype, AI agents are not autonomous overlords replacing human decision-making. They are tools that enhance efficiency and accuracy, but they still require human oversight for:

  • Edge Cases – AI struggles with grey areas where patterns aren’t clear-cut.
  • New Types of Fraud – AI can only detect fraud it has seen before. Fraudsters constantly evolve tactics, meaning humans must train AI on new threats.
  • False Positives – AI isn’t perfect. If too many legitimate transactions are blocked, customers get frustrated, and banks lose business.

AI agents augment human expertise rather than replace it, ensuring that complex decisions are subject to human judgment.

Beyond Fraud: Other AI Agent Use Cases in Business

AI agents are already transforming multiple industries, with applications beyond fraud detection. Here are a few:

1. AI Agents in Supply Chain Optimisation

  • Processing & Reasoning: AI predicts demand fluctuations, detects bottlenecks, and recommends inventory adjustments.
  • Decision Engine: Weighs cost, speed, and supplier reliability to optimise shipments.
  • Execution Layer: Automatically places supply orders or reroutes shipments based on real-time needs.

2. AI Agents in Cybersecurity Threat Detection

  • Processing & Reasoning: AI detects anomalies, scans for vulnerabilities, and assesses threats.
  • Decision Engine: Determines whether to block, flag, or escalate a security alert.
  • Execution Layer: Takes defensive actions—blocking suspicious IPs, isolating compromised systems, or notifying cybersecurity teams.

3. AI Agents in Customer Service Automation

  • Processing & Reasoning: AI assesses user intent and sentiment.
  • Decision Engine: Determines whether to respond automatically or escalate to a human.
  • Execution Layer: Provides real-time responses or transfers cases with full context.

Final Thoughts: The Reality of AI Agents in Business

AI agents are not theoretical—they are already delivering real impact in fraud detection, cybersecurity, supply chain management, customer service, and beyond. However, their effectiveness hinges on strategic deployment, quality data, and human oversight.

  1. AI models must operate within structured decision-making frameworks.
  2. Human oversight remains essential for refining AI’s learning process.
  3. AI agents are only as valuable as the supporting data and feedback loops.

Next time you see an AI agent headline claiming complete automation, ask yourself: Is this genuinely new technology or just a repackaging of existing technology?