What is an AI Agent? Definition, Types, Frameworks

ChatGPT can write you a poem. Claude can summarize your meeting notes. But ask them to cancel your 3 PM appointment, notify your team, and reschedule for the upcoming week, and they stop short. That’s because most AI today is generative; they don’t have access to your calendar, Gmail, or Contacts app. It responds to prompts but doesn’t take initiative. They use GenAI to provide replies based on a single interaction. A person asks, and the model answers using natural language processing (NLP). 

This is where the AI agent comes in, built for autonomy and goal-driven tasks. These systems don’t just generate responses. They observe, reason, act, and adapt, often without human oversight. In this blog, we’ll break down how AI is growing past writing emails and summaries, into hands-on intelligent autonomous action, and what that means for anyone using LLMs today.

Table of Contents

• What Is an AI Agent?
• The Three Layers of Intelligence: LLMs to Agentic AI
• How do AI Agents work?
• Types of AI Agents
• Components of an AI agent system
• Agentic AI Frameworks
• Real World Applications of Agentic AI
• Risks and Challenges of AI Agents

What is an AI Agent?

An AI agent is an autonomous system that can perceive its environment, make decisions, and take actions to achieve a specific goal. Unlike regular AI that just answers a single question and stops, agents work with context, memory, and goals. They can plan, adapt, and finish tasks without constant human input.

The Three Layers of Intelligence: LLMs to Agentic AI

Before we dive into how AI agents work, let’s take a step back. To truly understand agentic AI, it is helpful to examine the systems that came before it. We’re going to follow a simple progression, starting with LLMs like Claude & ChatGPT, moving through AI workflows, and ultimately arriving at AI agents that can perceive, reason, act, and improve autonomously.

Level 1: Large Language Models (LLMs)

At the foundation of today’s most popular AI tools, like Perplexity, ChatGPT, Claude, and Gemini, are large language models. They learn from enormous amounts of data to understand and produce text that sounds natural and human. You provide an input, the model predicts what comes next, and returns a response that feels coherent and useful.

Need a draft email? It will write one.

Need to know when your next meeting is? It won’t know.

It might even give you a generic response like “I can’t access your calendar.”

The reason boils down to two main limitations of LLMs.

• They don’t have access to your real-world tools or data.

LLMs operate on static knowledge. They can’t see your calendar, open your files, or retrieve live information unless you include it in your prompt.

• They don’t take initiative.

LLMs are reactive. They respond to instructions but don’t plan, decide, or act independently. By default, there is no persistent memory, no goal-setting, and no built-in autonomy.

You can extend what they do using plugins, APIs, or skillfully crafted prompts. But on their own, LLMs are just language machines. They generate responses but don’t carry out tasks. To move from conversation to execution, we need something more structured. That leads us to the next layer in the stack: AI workflows.

Level 2: AI Workflows

If LLMs generate responses, workflows help them complete tasks.

An AI workflow connects the model to tools and data. It breaks a task into steps and lets the model move through them with structure and logic.

Imagine you want to automate your daily newsletter curation. The workflow might look like this:

• New articles are pulled from your favorite sources
• The LLM summarizes each one
• The summaries are formatted into a clean layout
• The final version is sent through your email platform

Each step hands off to the next, and the whole thing runs without you lifting a finger. This setup makes the model useful in real-world tasks. It can access systems, pass data between apps, and follow a sequence.

But it still doesn’t decide anything on its own. The AI is following instructions, not goals. It doesn’t adjust if something changes, and it won’t know what to do if the process breaks. 

Workflows give structure. Agents give autonomy. That’s where we’re headed next.

Level 3: AI Agents

LLMs can generate. Workflows can be automated. But only agents can decide.

AI agents are systems designed to operate with autonomy. They don’t just respond. They gauge and take note of their environment, reason through options, take actions, and learn from what happens. The goal isn’t just output, it’s progress toward a purpose.

Think of an AI agent as something that can handle an entire objective end-to-end. 

Let’s say you ask an AI agent to book your next team offsite. It won’t stop at drafting an email. It can:

• Check your team’s calendar for available dates
• Search travel options based on your location
• Compare hotel prices and amenities
• Confirm bookings once everything lines up
• Follow up with team members who haven’t responded
  

In essence, AI agents incorporate LLMs with tools, memory, feedback loops, and decision logic. Some of the AI models utilize frameworks like React to determine the next step. Others rely on methods like RAG to fetch the right data just in time, helping them respond with context.

Unlike LLMs, agents don’t wait for instructions. And unlike workflows, they’re not locked into a fixed sequence. They adapt. They respond to change. They pursue a goal even if the path shifts halfway through.

This is what gives Agentic AI its name: the ability to act as an agent on your behalf, without constant human oversight. 

How do AI Agents work?

To see how agents operate, it helps to break their workflow into five steps. Each builds on the last, creating a cycle that lets them work with context, adapt in real time, and steadily improve.

1. Perceive

Agents begin by observing the world around them. That might mean reading sensor data, pulling from APIs, scanning documents, or analyzing user behavior. The goal is to extract what’s relevant and form a live understanding of the environment they’re in.

2. Reason

Once they’ve perceived the context, agents plan their next move. An LLM (large language model) typically sits at the core of this step, coordinating tasks, evaluating conditions, and determining the most sensible action. Sometimes that includes using tools like retrieval-augmented generation (RAG) to fetch custom or domain-specific data.

3. Act

Reasoning leads to execution. Agents interact with systems through APIs, tools, or interfaces—sending emails, submitting forms, running automations, or calling third-party services. Guardrails are often built in. For instance, an agent may process low-value claims automatically but flag anything over a threshold for human review.

4. Learn

This is where agentic systems separate themselves from static scripts. They evolve. Data from each interaction is captured, analyzed, and used to fine-tune behavior. Over time, the agent gets better at choosing the right actions, avoiding mistakes, and adapting to new scenarios.

5. Reflect

Reflection closes the loop. After learning from new data, the agent steps back to assess how its actions lined up with its goals. It reviews successes, identifies missteps, and adjusts its strategy for the next run. This self-check keeps progress intentional rather than accidental, helping the agent grow more effective with each cycle.

Types of AI Agents 

AI agents differ in how they perceive the world, what they aim to achieve, and how they decide what to do. Some respond to immediate inputs. Others work toward broader outcomes. Here’s how they’re typically categorized:

1. Simple Reflex Agents

They operate on basic condition and action rules. If something happens, they respond right away. No memory, no planning. Just direct reactions to current input.

2. Model-Based Agents

These agents maintain a limited internal state. That model helps them make sense of what’s happening, even when all the information isn’t visible.

3. Goal-Based Agents

They choose actions based on whether those actions bring them closer to a defined goal. Planning and assessment are central to how they operate.

4. Utility-Based Agents

When multiple paths could reach a goal, these agents assess which one leads to the most beneficial outcome. It’s not just about finishing—it’s about doing it well.

5. Learning Agents

They adjust over time. With feedback and results, they refine how they interpret input and make decisions, improving with continued use.

6. Knowledge-Based Agents

These agents use structured information and logical reasoning to solve problems. They can explain their actions and rely on a base of factual knowledge to guide them.

We’ve broken each of these down in detail in this post if you want to dive deeper.

But whatever the type, most real-world AI agents today are powered by agentic frameworks that help them make decisions and act autonomously. Let’s take a look at how.

Components of an AI agent system

Agentic AI is more than a single model responding to prompts. It is an integrated system where each component plays a specific role in enabling perception, decision-making, and autonomous action.

Perception

Every intelligent system begins with awareness of its environment. AI agents gather and interpret information from various sources such as sensors, application data, system logs, or user input. This raw data is transformed into meaningful signals that allow the agent to understand the current state and identify what matters most for the task at hand.

Reasoning and Planning

Once the environment is understood, the agent evaluates the situation, defines objectives, and determines a path forward. Large language models often operate as the reasoning engine, breaking down objectives into actionable steps. In some cases, techniques like retrieval augmented generation are applied to pull in domain-specific knowledge before creating a plan.

Action Execution

Plans must translate into results. This is the execution phase, where the agent interacts with tools, software platforms, or connected systems through application programming interfaces. Well-designed agents incorporate governance mechanisms, ensuring that actions are accurate, compliant, and aligned with intended goals.

Memory and Learning

Autonomous operation improves with experience. Agents retain contextual information, store the outcomes of previous actions, and adapt based on what they learn. This feedback loop allows them to refine their approach over time, respond to changing conditions, and deliver progressively better results.

Agentic AI Frameworks 

So far, we’ve talked about what AI agents are, their types, and what they can do. But how does that decision-making work? To operate autonomously, AI agents rely on structured and systematic reasoning frameworks that help them navigate complex, multistep environments. Among these, two core ones are ReAct and RAG.

Reasoning and Acting (ReAct) 

To operate independently, AI agents follow structured reasoning models that help them navigate multistep processes. One of the most prominent is ReAct, short for Reasoning and Acting. This framework allows agents to work in cycles. They act, examine the result, and then decide what to do next. Instead of planning everything up front, they move step by step, adjusting their choices as new information appears.

This method is especially useful when agents must respond to changing inputs or incomplete data. ReAct is often built using a method called the chain of thought, where the agent explains its thinking before acting. This makes it easier to follow the logic and understand why a decision was made. ReAct supports transparency, and that makes it suitable for tasks where the reasoning process matters just as much as the outcome.

Retrieval Augmented Generation (RAG)

While ReAct helps agents think in steps, RAG solves a different problem. Most language models only know what they were trained on. They cannot access new information, private data, or live sources. RAG, which stands for Retrieval Augmented Generation, bridges that gap. It lets an agent search external sources like documents, websites, or internal company data before answering a question.

This gives the agent context that would otherwise be missing. Once the right content is pulled in, the model uses it to produce a more accurate and relevant output. RAG is useful when responses need to be grounded in facts or based on real-time data. It supports a great range of practical use cases, from customer service bots to legal research agents, where the quality of the answer depends on what the agent can access, not just what it remembers.

Real World Applications of Agentic AI

Across industries, agentic AI is transforming how complex tasks get done. These systems combine perception, reasoning, and autonomous decision-making to reduce human intervention and improve efficiency at scale. Let’s look at where they’re already creating real value.

1. Claims Processing in Insurance

AI agents can now manage the end-to-end claims process. When a claim is submitted, the agent validates inputs, fetches supporting documents from multiple systems, and sends follow-up queries to customers when needed. Human adjusters step in only for edge cases, while agents keep everything else moving. This reduces processing times and frees up staff to focus on higher-value cases such as nuanced fraud detection or dispute resolution. In a field where customer trust hinges on speed, shaving hours off claim cycles can drive real competitive advantage.

2. Operational Intelligence in Logistics

For logistics teams, even a few minutes of delay can cascade into capital loss across the chain. Agentic AI brings the ability to replan routes, adjust schedules, and reroute deliveries based on real-time signals from traffic, inventory, and demand forecasts.
These agents respond to shifting conditions faster than any dashboard refresh, which can result in fewer delays, tighter coordination, and a smarter supply chain that runs with minimal intervention.

3. Risk Monitoring in Financial Services

AI agents now work behind the scenes in financial institutions to scan internal data, news feeds, and transaction patterns. They flag emerging risks, monitor thresholds, and even help teams stay audit-ready.
This autonomous and structured approach reduces human blind spots and helps institutions get ahead of issues before they become crises. In finance, a missed anomaly isn’t just an oversight; it can be a million-dollar mistake.

4. Discovery Acceleration in Life Sciences

Drug discovery is traditionally slow, costly, and uncertain. Agentic AI changes the pace by ingesting scientific literature, research datasets, and clinical trial outputs to identify promising compounds and predict their effectiveness.
These agents help scientists narrow their focus and run fewer dead-end experiments. It’s like having a 24/7 research assistant that never sleeps and gets smarter with every trial.

5. Support Automation in Customer Experience

Gone are the days of static chatbots. Agentic AI now powers support systems that remember past interactions, adapt in real time, and resolve issues without escalation. These agents handle ticket triage, respond to complicated queries, and even trigger backend workflows.
Customers get personalized support, and teams spend less time on repetitive tasks. When support is proactive and always available, satisfaction and loyalty follow naturally.

6. Software Testing and Quality Assurance

In modern software development, testing is no longer just about pass or fail. Agentic AI supports engineering teams by generating smarter test cases, identifying bugs early, and suggesting fixes based on past patterns.
These agents evolve with the product, helping teams move faster without sacrificing reliability. In fast-paced release cycles, intelligent testing can be the difference between stability and downtime.

Risks and Challenges of AI Agents 

While agentic AI unlocks new possibilities for productivity and decision-making, it also brings new risks. As these systems move from passive responders to autonomous operators, the potential for impact (both positive & negative) grows. Responsible adoption demands a closer look at the challenges that come with giving AI more control.

1. Autonomy without accountability
   Giving AI agents the capacity to act on their own introduces a serious question of oversight. If an agent makes an error, who is responsible? Without clear review mechanisms or defined escalation points, automated decisions can go unchecked. Guardrails, approval steps, and transparent logic flows are not just technical features; they are ethical and legal necessities.
   
2. Opaque reasoning and cascading errors
   Unlike chatbots that produce isolated outputs, agentic systems make sequential decisions. If the elementary reasoning is flawed, every following step may inherit that flaw. Since many Artificial Intelligence models can sound confident even when wrong, this compounds the problem. Without visibility into how and why a decision was made, users may not even know something went wrong.
   
3. Security and access control
   To perform tasks, agents often need access to calendars, documents, databases, or financial records. This creates new attack surfaces and vulnerabilities. Strict authentication, granular access rights, and activity monitoring must be baked into every layer of deployment. Where sensitive information is involved, rules-based systems or robotic agents may be a safer option for handling those tasks.
   
4. Data exposure and compliance
   Autonomous systems can move across tools, departments, and data silos in seconds. That flexibility is powerful, but it also raises flags for regulatory compliance. Without careful design, agents may expose confidential data or bypass important checks. Clear permissions, role-based access, and audit logs are critical to maintaining trust and avoiding violations.

5. Bias and unintended outcomes
   AI agents learn from past data, and that data can reflect social and systemic biases. If those biases go unchecked, the agent might reinforce them in high-stakes environments like hiring, healthcare, or finance. The results can impact real people and cause real harm. Continuous audits, bias mitigation strategies, and ethical guardrails are key to making sure agents act in the interest of fairness and accountability.

Conclusion- A Future Led by Intelligent Autonomy

Agentic AI is more than an upgrade to automation. It signals a shift from systems that wait for commands to those that move with purpose. These agents sense, reason, and respond in context, adapting as conditions evolve. With the right checks and direction, they can extend human capability, drive sharper decisions, and open new frontiers in how we work and build.

If agentic AI feels like the future knocking but you’re still figuring out where to start, this Agentic AI course for non-tech professionals might help. No technical background required. Just practical learning designed for anyone who wants to work with AI agents.