AI Agents vs Agentic AI

Your first introduction to Artificial Intelligence (AI) is most likely ChatGPT, a tool that has transformed how we interact and solve problems. Apart from ChatGPT, there’s a trove of Artificial Intelligence systems with different capabilities yet to be explored. 

AI can be applied in various forms. For example, many organizations use AI agents for specific tasks such as data analysis, cybersecurity, and network monitoring to detect anomalies in network traffic.

These AI agents are also evolving, becoming more independent in decision-making rather than simply following human instructions. 

In this article, we will discuss AI agents and Agentic AI to understand how they differ and perform tasks.

TL; DR

AI agents are reactive systems that perform tasks based on predefined rules or prompts. Agentic AI is proactive and autonomous; it can plan, reason, make decisions, execute actions, use tools, learn from outcomes, and adapt based on feedback to handle tasks independently and efficiently. 

AI Agent

AI agents are software systems that perform specific tasks or actions based on user instructions or in place of a user. Think of AI agents as assistants that can perform specific tasks based on predefined rules or parameters. 

An AI agent typically handles a single task; in other words, they are designed for goal-specific use rather than general-purpose use. For example, a customer support agent reads users' tickets and returns responses. This means that it can handle multiple tasks related to customer support only. 

Importance of AI Agents

There are different reasons why AI agents are important, including: 

Productivity Boost:  AI agents can help improve employee productivity by handling routine tasks that consume time. They can automate repetitive tasks such as invoice generation, email replies, and more, giving employees more time to focus on higher-priority and more complex tasks.

Operational Efficiency: Assigning specific tasks to an AI agent can reduce resources spent on manual work, speed up processes, and improve the operations or overall workflow of an organization.

AI agents can also handle workloads that a single person may not be able to manage entirely in terms of speed and consistency. 

Saving Cost: AI agents make automating tasks and processes simpler, and can help reduce operational costs because they require little or no human intervention. They can perform tasks faster and more consistently, increasing productivity. 

How are AI agents different from generative AI?

Generative AI is a type of Artificial Intelligence where models are designed to create text, images, videos, code, and more by learning patterns from existing data. They are generally considered reactive systems because they wait for a prompt and then generate a response based on what they’ve been trained on, rather than acting independently. 

For example, Claude AI can write a formal letter when prompted. If a user instructs it to "write a formal letter to express gratitude", the model, trained on a large number of human-written texts such as emails, letters, and stories, generates a letter by using the phrases, tone, patterns, and structures it learned during training. 

Models like ChatGPT cannot fetch information about recent events or understand new events unless that information is provided. However, they can access recent data using a browser tool or plugin integrated with the model. 

AI agents, on the other hand, are designed for goal-specific tasks. They follow predefined instructions that guide how they process inputs and execute actions. They perform tasks based on human instructions and can also execute tasks autonomously within specific boundaries and instructions defined by humans.  

Types of AI Agents 

Here are types of AI agents based on behaviour, including:

Simple Reflex Agents: These agents are considered the basic type of AI agents. They operate based on specific constraints or condition-action rules to make decisions without considering experience or future consequences. An example is an automatic door that opens and closes when it detects motion. 

Model-based Reflex Agent:  Model-based reflex agents are more advanced than simple reflex agents. These agents use condition-action rules and maintain an internal model of the environment, which tracks recent state and past interactions to make informed decisions. 

An example is a self-driving car that monitors current road and traffic conditions to stay in the right lane. 

Goal-based agents: These agents are more proactive and take a goal-oriented approach to problem-solving. They examine their objectives and use planning and reasoning to guide their actions towards achieving a specific goal.  An example is a navigation map that plans routes to a destination.

Utility-based Agents: These AI agents are designed to handle more complex decision-making tasks. They make smart decisions by evaluating multiple actions, predicting consequences, and deciding the best step to achieve a goal.

This approach allows them to make decisions. An example is a trading Systems that evaluate trades and predict outcomes

Learning Agents: Learning agents modify their behaviour based on feedback from their environment. They also rely on predefined rules or models, but they can improve their decision-making over time, especially in uncertain or complex situations. An example is a customer support chatbot that learn from user interactions to provide better responses.

Agentic AI

Agentic AI is a system designed to handle tasks independently compared to traditional AI agents. They are Proactive and autonomous, meaning they can make decisions, plan, and take actions to achieve goals without human intervention. 

They can also refine their behaviour over time in response to environmental changes.  

The term "Agentic AI" was introduced by AI researcher Andrew Ng in 2024 to describe systems that can create and innovate independently, just like how humans would. 

They can understand context and instructions, set goals, adapt, and take actions based on how conditions change. 

Agentic system follows this workflow:

• Perceives its environment  

• Decide on an Action 

• Execute an Action 

• Learn from the Output. 

A typical Agentic workflow may look like this;

• The user provides a task E.g., "Write an excerpt about the solar system." 

• The agent understands the goal and breaks it down (using train of thought reasoning) 

• The agent selects a tool, such as a web search tool

• Based on user instructions, the agent can retrieve the top 5 relevant sources from the web search 

• The agent evaluates the sources and then selects the most relevant one 

• The agent creates an initial draft based on the selected source

• The agent reflects on the draft, checking accuracy, clarity, and more.

• The agent delivers the final output, which is an accurate excerpt.

What powers an Agentic System?

Behind an Agentic system lies a large language model. 

A large language model (LLM) is considered the brain or the reasoning engine of agentic systems.  A Large Language Model (LLM) is capable of understanding instructions and generating responses, but when it is combined with:

• External tools (such as a web search plugin)

• APIs and databases

• Planning capabilities

• Memory systems 

• Sate management

It is transformed into something that can reason, plan, take action, and execute tasks.

This integration is what makes an agentic system capable of setting goals, following structured actions with minimal human input, unlike a typical chatbot that responds to user prompts and generates single responses.

Components of Agentic AI

Planning and Execution:  Agentic AI systems do more than react to commands. This component allows systems to determine how to achieve a goal by breaking it into manageable tasks and deciding the steps required. 

The AI system decides the appropriate tools, order of actions, and also evaluates potential obstacles. After planning, it executes, monitors progress, and updates as required. 

Memory and State Tracking: This component allows AI systems to remember the past and track events in real time. By maintaining state, the system can use past experiences to make future decisions. 

Advanced memory systems help the AI avoid repeating mistakes and learn from what worked or failed, improving performance over time.

Perception and Environmental Awareness: Agentic AI systems can interpret activities in their environment via visual, audio, and textual inputs. This enables them to identify trends, understand context, recognize patterns, and use real-time monitoring to make informed decisions instead of relying on predefined input. 

Learning and Adaptation Engine: AI systems evaluate and observe the outcomes of actions and use the feedback to make more accurate future decisions. They review the result of previous actions, refine strategies, and adapt the approach where necessary. 

Communication and Interaction: This component enables the AI system to collaborate with humans and other AI systems / external tools. It allows the agent to exchange information, determine, and plan actions to achieve and improve its objectives. 

Decision-Making and Control System: This component is responsible for the agent's mechanism. It brings all components, including planning, memory, perception, communication, and learning, ensuring the AI behaves appropriately and performs actions based on the defined goal.

Benefits of Agentic AI

Parallelism: Parallelism in an agentic workflow means the agent performs multiple independent tasks simultaneously. For example, the agent can search different sources concurrently and evaluate them in parallel before deciding which information to use.  This reduces the time required to complete tasks and improves efficiency. 

Perform Multiple Tasks Effectively: Agentic AI can understand and break down complex goals into smaller tasks and decide how it wants to accomplish them to achieve a final result. The agent understands context and priorities and selects the right tools required for the tasks. 

Modularity: Agentic AI enables complex goals to be broken down into independent modules responsible for specific functions, such as planning, reasoning, or evaluation. 

Each module operates independently and can be reused across different workflows, making it easy to develop, maintain, and upgrade workflows without interrupting the entire system. This modular structure supports the development of flexible, scalable, and resilient systems.  

Types of Agentic AI 

There are different types of Agentic AI systems. They can be categorised based on autonomy, complexity, and applications. Here are some common types:

Autonomous Single-Agent System: This AI agent operates independently without other agents. It can plan, make decisions, and execute tasks within predefined boundaries. For example, a customer support agent may resolve queries, update CRM records, and send follow-up emails. 

Multi-Agent Systems:  Multi-agent systems consist of multiple agents that collaborate to achieve shared or individual goals. They are used in complex environments where a single agent system may not be able to handle task such as a resource management system or large-scale automation systems. 

The different multi-agent systems include:

• Vertical multi-agent systems: In this structure, the AI agent acts as a manager; delegates tasks to supporting agents, monitors progress, and ensures that the overall goal is achieved. 

• Horizontal multi-agent systems: This involves agents that work together as peers. Each agent specializes in a specific task and collaborates to solve problems and make decisions collectively. 

Human-in-the-Loop Agentic Systems: Human-in-the-loop agentic systems involve AI agents that operate autonomously but are overseen by humans when making critical decisions.

The agent manages part of the workflow, makes decisions, and also seeks approvals when necessary. It is used in high-risk environments such as healthcare or finance. 

Agentic AI VS Traditional AI Agents

Summary

It is said that Agentic AI systems are AI agents, but not all AI agents are agentic. The difference lies in their capability.

Traditional AI agents typically follow predefined rules or respond to user prompts. On the other hand, the goal of agentic AI is to build systems that require minimal human guidance, incorporate planning, tool use, memory, feedback loops, improvement, and decision-making.

Modern workflows can combine traditional AI agents with agentic systems. Traditional agents can handle routine tasks, while agentic systems can handle tasks requiring complex planning, decision-making, and execution.

Frequently Asked Questions