An AI agent is

But that is not all: An agent is something more than a predictive or classification model; rather, it is an autonomous system that may take an action directed towards some goal.

Put differently,

An AI agent processes information, but it doesn’t stop there. It’s in the comprehension, the memory, and the goals that will determine what comes next.

Let’s consider three key capabilities of an AI agent:

• Perception: It collects information from sensors, APIs, documents, user prompts, amongst others.
• Reasoning: It knows context, and it plans or decides what to do next.
• What it does: Performs an action list; this can invoke another API, write to a file, send an email, or initiate a workflow.

A classical ML model could predict whether a transaction is fraudulent.

But an AI agent could:

• Detect suspicious transactions,
• Look up the customer’s account history.
• Send a confirmation email,

Suspend the account if no response comes and do all that without a human telling it step by step.

Under the Hood: What Makes an AI Agent “Agentic”?

Genuinely agentic AI systems, by contrast, extend large language models like GPT-5 or Claude with more layers of processing and give them a much greater degree of autonomy and goal-directedness:

Goal Orientation:

• Instead of answering to one prompt, their focus is on an outcome: “book a ticket,” “generate a report”, or “solve a support ticket.”

Planning and Reasoning:

• They split a big problem up into smaller steps, for example, “first fetch data, then clean it, then summarize it”.

Tool Use / API Integration:

• They can call other functions and APIs. For instance, they could query a database, send an email, or interface to some other system.

Memory:

• They remember previous interactions or actions such that multi-turn reasoning and continuity can be achieved.

Feedback Loops:

• They can evaluate if they succeeded with their action, or failed, and thus adjust the next action just as human beings do.

These components make the AI agents feel much less like “smart calculators” and more like “junior digital coworkers”.

A Practical Example

Now, let us consider a simple use case comparison wherein health-scheme claim analysis is close to your domain:

In essence, any regular ML model would take the claims data as input and predict:

→ “The chance of this claim being fraudulent is 82%.”

An AI agent could:

• Check the claim.
• Pull histories of hospitals and beneficiaries from APIs.
• Check for consistency in the document.
• Flag the anomalies and give a summary report to an officer.
• If no response, follow up in 48 hours.

That is the key shift: the model informs, while the agent initiates.

Why the Shift to Agentic AI Matters

Autonomy → Efficiency:

• Agents can handle a repetitive workflow without constant human supervision.

Scalability → Real-World Value:

• You can deploy thousands of agents for customer support, logistics, data validation, or research tasks.

Context Retention → Better Reasoning:

• Since they retain memory and context, they can perform multitask processes with ease, much like any human analyst.

Interoperability → System Integration:

• They can interact with enterprise systems such as databases, CRMs, dashboards, or APIs to close the gap between AI predictions and business actions.

 Limitations & Ethical Considerations

While agentic AI is powerful, it has also opened several new challenges:

• Hallucination risk: agents may act on false assumptions.
• Accountability: Who is responsible in case an AI agent made the wrong decision?
• Security: API access granted to agents could be misused and cause damage.
• Over-autonomy: Many applications, such as those in healthcare or finance.

do need human-in-the-loop. Hence, the current trend is hybrid autonomy: AI agents that act independently but always escalate key decisions to humans.

Body Language by Jane Smith

“An AI agent is an intelligent system that analyzes data while independently taking autonomous actions toward a goal. Unlike traditional ML models that stop at prediction, agentic AI is able to reason, plan, use tools, and remember context effectively bridging the gap between intelligence and action. While the traditional models are static and task-specific, the agentic systems are dynamic and adaptive, capable of handling end-to-end workflows with minimal supervision.”