Artificial Intelligence has gone through several evolutionary stages: from rule-based systems, to machine learning models, to large language models (LLMs) capable of reasoning over complex information. Today, we are entering a new phase: AI Agents.

AI agents are rapidly becoming one of the most discussed topics in the AI ecosystem, not only because they can think, but because they can also act. In a recent webinar with Dr. Toqeer Ali Syed, Professor at the Islamic University of Madinah and expert in machine learning, trusted computing, blockchain, and AI systems, this shift from intelligence to autonomy was explored through multiple real-world applications. This article builds on those ideas to provide a broader and structured introduction to AI agents.

What Are AI Agents?

At a high level, an AI agent is a system that combines:

• Reasoning (usually powered by LLMs)

• Decision-making

• Action execution

• Autonomy, with optional human oversight

Traditional LLMs can analyze text, generate answers, and provide recommendations. However, they are passive: they do not take actions on their own. AI agents extend this capability by linking reasoning with tools, APIs, workflows, and external systems.

In simple terms:

LLMs think. AI agents think and act.

An AI agent can:

• Interpret user intent

• Plan a sequence of steps

• Interact with external systems (APIs, databases, sensors)

• Execute actions

• Observe outcomes and adapt

Why AI Agents Matter Now

Several technological trends have converged to make AI agents feasible and impactful:

1. Powerful LLMs capable of reasoning and planning

2. Agent frameworks (e.g., LangChain, LangGraph) that orchestrate actions

3. Standardized APIs and protocols enabling tool usage

4. Human-in-the-loop mechanisms for safety and governance

AI agents are especially relevant in environments where:

• Decisions are repetitive but context-dependent

• Multiple systems need to be coordinated

• Real-time or near-real-time actions are required

Dr. Toqeer Ali Syed’s research journey provides a useful lens to understand why AI agents are not an isolated innovation, but part of a broader evolution.

Dr Syed’s earlier work focused on trusted computing, cybersecurity, and blockchain-based decentralized trust. These domains addressed a fundamental problem:

How can systems operate securely and reliably without relying on a single central authority?

This question is just as relevant for AI agents today.

As systems become more autonomous, trust, transparency, and traceability become essential. This is why modern agent architectures often integrate:

• Blockchain for immutable logs of agent actions

• Permissioned systems for accountability

• Explainable reasoning pipelines 

Core Components of an AI Agent Architecture

A typical AI agent system includes:

1. Input Layer

User requests, sensor data, system events, or historical data

2. Reasoning Layer (LLM)

Interprets context, plans actions, evaluates alternatives

3. Agent Layer

One or more agents responsible for specific tasks

• Single-agent systems (simple workflows)

• Multi-agent systems (collaborative or hierarchical tasks)

4. Action Layer

APIs, databases, smart contracts, IoT devices, enterprise systems

5. Oversight Layer

Human-in-the-loop validation, logging, and monitoring

Frameworks such as LangChain support single-agent reasoning, while LangGraph enables multi-agent collaboration, where the output of one agent becomes the input of another.

Real-World Use Cases of AI Agents

The webinar showcased how AI agents can be applied across industries. Below are generalized examples inspired by those applications.

1. Software Supply Chain Security

AI agents monitor code repositories, dependencies, and CI/CD pipelines.

They can:

• Detect anomalies or vulnerabilities

• Verify code provenance

• Record decisions transparently (e.g., via blockchain)

• Prevent risky deployments automatically

2. Smart Cities and Infrastructure

Agents combine IoT data, digital twins, and AI analytics to:

• Detect infrastructure failures (roads, pipes, utilities)

• Create service tickets automatically

• Notify authorities before issues escalate

3. Healthcare and Assisted Living

AI agents monitor dietary intake, health metrics, and behavior patterns to:

• Track calories and nutrition

• Suggest personalized diets

• Alert caregivers when intervention is needed

4. Financial Planning and Budgeting

Personal finance agents can:

• Analyze income and fixed expenses

• Optimize monthly budgets

• Adapt recommendations based on real-time prices

• Execute actions such as ordering groceries within constraints

5. Disaster Prediction and Emergency Response

Sensing agents analyze atmospheric and environmental data to:

• Predict extreme weather events

• Alert rescue services in advance

• Coordinate early evacuation or mitigation actions

6. Supply Chain and Inventory Management

Agents monitor stock levels and consumption patterns to:

• Predict shortages

• Automatically place orders via supplier APIs

• Reduce waste and operational delays

Human-in-the-Loop: A Critical Design Principle

Despite their autonomy, AI agents should not operate unchecked.

Most production-grade systems adopt a human-in-the-loop approach:

• Humans validate critical decisions

• Agents propose actions, not final authority

• Errors can be corrected before execution

This balance between automation and control is essential for safety, ethics, and compliance.

Challenges and Open Questions

While promising, AI agents raise important challenges:

• Security: Preventing malicious or unintended actions

• Transparency: Understanding why an agent acted in a certain way

• Scalability: Coordinating multiple agents efficiently

• Governance: Defining responsibility and accountability

Ongoing research, including work like that presented in the webinar, is actively addressing these issues.

Conclusion: The Future Is Agentic

AI agents represent a shift from intelligent tools to autonomous systems. They are not just answering questions—they are executing workflows, coordinating systems, and making decisions in complex environments.

As Dr. Toqeer Ali Syed’s work illustrates, the future of AI lies at the intersection of:

• Intelligence

• Autonomy

• Trust

• Transparency

Organizations that understand and adopt agent-based architectures today will be better positioned to build scalable, resilient, and trustworthy AI systems tomorrow.

You can review the presentation of Dr. Toqeer Ali Seed here: