Agentic AI vs AI Agents

Artificial Intelligence (AI) keeps advancing, bringing in new ideas that change how machines interact with the world. Two of these ideas, Agentic AI and AI Agents, are often mixed up because they share similar features. However, they play different roles and work within different frameworks.

Agentic AI is a more advanced and autonomous form of AI. It can function on its own, handling complex tasks with minimal human input. It adapts to changes in its environment, makes its own decisions, and improves processes over time. On the other hand, AI agents are designed for specific tasks. They excel at repetitive or structured jobs but don't have the ability to adapt or think independently like Agentic AI.

How do they work?

AI Agents are like workers who follow instructions. They take in information, process it step by step, and give you a result. For example, if you ask an AI Agent to calculate the sum of two numbers, it will take the numbers, add them, and give you the answer. It works alone and does only what it’s told. (to make it simpler? They will respond to your questions, single-handedly)

On the other hand, Agentic AI is more like a team of workers who talk to each other and solve problems together. It’s not just one agent doing a single task. Instead, it’s a network of agents working as a group. For example, if Agentic AI is used to manage traffic in a city, it will analyze data from many sources (like traffic cameras and sensors), share that information, and make decisions to reduce traffic jams. It’s not just one agent working alone; it’s many agents working together. (to make it simpler? They will perform a task, and make decisions as a team of multiple agents together)

1. Level of Autonomy

• AI Agent function within strict guidelines. They process inputs and respond based on preprogrammed rules, making them predictable but limited.
• Agentic AI goes beyond executing commands. It determines objectives, refines strategies, and adapts based on external factors, making it more flexible and dynamic.

Let me provide more details with the example

An AI agent in customer support is typically programmed to follow a set of pre-defined rules or scripts. When a customer asks a question, the AI agent can pull from a database of responses and provide answers based on that fixed script. For instance, if a customer asks about store hours, the agent might respond with, "Our store is open from 9 AM to 6 PM every day." It doesn't have the capability to think or adapt to new situations. If a customer asks something outside the script, the agent might struggle or escalate the issue to a human.

On the other hand, Agentic AI takes a more advanced approach. It can understand not just the specific words a customer uses but also the sentiment behind them—whether the customer is happy, frustrated, or confused. Agentic AI would not only answer the query but also prioritize urgent issues, such as a complaint about a malfunctioning product. Over time, it could learn from past interactions, improving its ability to handle customer concerns more effectively. For example, it might realize that customers are often frustrated when their issues are not resolved quickly, and it could prioritize these types of cases. Additionally, it might refine its responses by learning what tone or wording works best to calm customers down.

In summary

• AI agents follow scripts and are task-specific, working within fixed boundaries.
• Agentic AI is more autonomous, capable of learning, adapting, and improving decision-making in real-time.

2. Decision-Making Ability

• AI Agents rely on conditional logic or statistical models to make decisions. Their responses depend on predefined workflows.
• Agentic AI engages in higher-order reasoning. It evaluates situations, considers multiple pathways, and selects the best course of action based on real-time data.

Let me provide more details with the example

AI Agent in Self-Driving Cars:

An AI agent in a self-driving car is responsible for handling routine, predictable tasks based on predefined rules and routes. For example, the car may rely on sensors and algorithms to follow lane markings, maintain speed, stop at red lights, and stay on predefined routes. It can handle typical road conditions with precision. However, its capabilities are limited to the data it's been given and the specific actions it has been programmed to take.

For instance, the car might follow a straight highway and stay within the lines, but if a road is blocked or there’s an unexpected detour, the AI agent may need human intervention or a manual override. It doesn’t have the flexibility to make decisions beyond its programming—it’s not prepared for situations outside the set of predetermined rules or conditions.

Agentic AI in Self-Driving Cars:

An Agentic AI vehicle is far more sophisticated and capable of thinking and adapting in real-time. It doesn't just follow the road and predefined routes; it actively anticipates and adapts to the environment around it, making dynamic, independent decisions based on new information. This means it can respond to unforeseen circumstances such as a pedestrian suddenly crossing the road or an obstacle like a fallen tree.

In summary

• Anticipating Pedestrian Movement: An Agentic AI in a self-driving car could predict that a pedestrian might cross the street based on their position, walking speed, and patterns of behavior (e.g., a person waiting at the curb near a crosswalk). The car can then slow down or even stop preemptively, without needing explicit input.
• Adjusting to Unexpected Obstacles: If a large object, like a truck, unexpectedly pulls into the lane, the Agentic AI can decide to safely switch lanes or brake suddenly if needed. It might also choose to reroute the vehicle based on real-time traffic and weather conditions, making intelligent decisions to avoid delays or accidents.
• Making Split-Second Decisions: In scenarios like an emergency vehicle approaching with its lights flashing, an Agentic AI car can recognize the urgency and prioritize clearing the lane or finding a safe space to pull over, without being explicitly programmed for that specific situation.

Key Difference:

• AI Agent: Follows programmed rules and processes predictable actions like staying in lane and obeying traffic signs, but it lacks flexibility for unexpected situations.
• Agentic AI: Makes decisions on the fly, adapting to changes in the environment, anticipating future events, and handling complex or unforeseen situations without human intervention.

3. Adaptability and Learning

• AI Agents do not improve significantly over time unless retrained by developers. They perform the same tasks in the same way unless updated.
• Agentic AI learns continuously from interactions. It refines its understanding, optimizes strategies, and adjusts to changing environments without direct reprogramming.

Let me provide more details with the example

AI Agent in Chatbots:

An AI agent in a chatbot is typically designed to follow a set script and respond to frequently asked questions (FAQs) using pre-programmed answers. It can handle common inquiries like “What are your business hours?” or “How can I reset my password?” with accurate, predefined responses. These chatbots are highly efficient at delivering quick and consistent answers to questions that have clear, repetitive answers.

However, while the AI agent can provide answers to simple queries, it does not learn or adapt from its interactions. If a user asks a question that falls outside the scope of the pre-set script, the chatbot might either fail to answer or escalate the issue to a human representative. Additionally, if a customer keeps asking the same question, the chatbot will repeat the same answer without adjusting based on context, tone, or past interactions.

For example:

• A customer asks, “What are your hours today?” and the chatbot responds with, “Our business hours are 9 AM to 5 PM.”
• If the customer asks the same question the next day, the chatbot will provide the same response, without improvement or learning from the past interaction.

Agentic AI in Chatbots:

An Agentic AI chatbot, however, is much more advanced and adaptable. It doesn’t just provide static responses to a set of predefined questions. Instead, it analyzes user interactions and uses that data to continuously refine and enhance its responses over time. The chatbot is capable of learning from each conversation, improving its accuracy and even predicting or proactively suggesting relevant information that the user might find helpful.

In summary

• Analyzing User Interactions: If a customer expresses frustration (e.g., “I’ve asked this three times already, why isn’t this working?”), the Agentic AI chatbot can pick up on the tone of the conversation, recognize the sentiment, and adapt its response to show empathy, such as: “I’m sorry for the repeated confusion! Let me try to assist you in a different way.”
• Refining Responses: As the chatbot interacts with more users, it becomes better at understanding subtle variations in questions and providing more personalized responses. If multiple users ask slightly different versions of the same question, it learns to recognize the different ways the question can be asked and provide the correct answer every time, even if the phrasing changes.
• Proactively Suggesting Information: Instead of just waiting for the user to ask a question, the Agentic AI chatbot can proactively offer helpful suggestions. For example, if a user is browsing a product page, it might suggest related items or helpful tips like, "You might also like these accessories," or "Here’s a user guide for this product."

Key Differences:

• AI Agent Chatbot: Responds to queries based on predefined scripts. It handles basic and repetitive tasks well but doesn’t adapt to the user's needs or improve over time.
• Agentic AI Chatbot: Learns from each interaction, refines responses to be more personalized and context-aware, and can proactively offer information. It adapts to the user’s needs and continuously improves its performance.

4. Goal-Oriented Behavior

• AI Agents follow step-by-step instructions but do not set their own objectives. Their actions serve immediate, predefined tasks.
• Agentic AI actively pursues high-level goals. It decomposes complex tasks into smaller actions, self-corrects, and iterates toward long-term objectives.

Let me provide more details with the example

AI Agent in a Recommendation System

An AI agent in a recommendation system typically suggests products based on past behavior or purchases. It looks at the data about what a user has bought in the past or what other users with similar preferences have purchased, then uses that to suggest similar products. The algorithm is often static and primarily reacts to what has already happened, with little ability to think beyond the existing patterns.

For example:

• If a customer buys a pair of running shoes, the AI agent might suggest other athletic footwear or related products like socks or fitness trackers based on past purchase history.
• If a user watches a certain genre of movies (say, action films), the recommendation engine might suggest other action movies based on what similar users have watched.

Agentic AI in a Recommendation System:

Agentic AI, on the other hand, is much more sophisticated and dynamic. It doesn’t just suggest products based on past purchases—it takes a more proactive approach to predict future needs and personalize recommendations in real-time.

For example:

• Predicting Trends: Agentic AI can analyze large datasets (including customer behavior, market trends, seasonal patterns, etc.) to predict emerging trends. For instance, if it notices a rising interest in smart home devices across a broad segment of users, it might start recommending these products to customers before the user explicitly shows interest. It doesn’t wait for the user to search for these items—it anticipates the growing demand and offers suggestions accordingly.
• Identifying User Needs Before They Arise: If a customer has been browsing fitness equipment and viewing various diet-related items but hasn’t purchased anything yet, the Agentic AI can identify a potential shift in their lifestyle. It might proactively recommend a personalized fitness plan, workout gear, or nutrition supplements. The AI recognizes that the user might be gearing up for a fitness journey and tailors its recommendations to meet this need before the user has even fully articulated it.
• Personalizing Recommendations Dynamically: As the user interacts with the platform, Agentic AI continuously refines and personalizes suggestions. If the customer purchases a new set of running shoes, the AI might suggest not only accessories (like a gym bag or water bottle) but also recommend related content, like running guides or community challenges. Additionally, it might suggest complementary products in real-time, adjusting to the user’s preferences, activity level, and previous interactions.

Key Differences:

• AI Agent in a Recommendation System: Relies on past purchases or behavior to suggest similar products. It's reactive, offering recommendations based on what the user has already done or what other similar users have done. It doesn’t anticipate future needs or adapt its suggestions dynamically in response to changes in user behavior.
• Agentic AI in a Recommendation System: Goes beyond static recommendations and predicts future trends or needs. It personalizes suggestions dynamically based on real-time user behavior, patterns, and broader market insights. It proactively identifies and addresses user needs, even before the user explicitly recognizes them.

Example Comparison:

• AI Agent Example: A user buys a laptop, and the system recommends laptop accessories like cases and chargers, based purely on the fact that the user bought a laptop.
• Agentic AI Example: A user buys a laptop, and the system not only recommends laptop accessories but also proactively suggests related productivity software, tutorials on how to optimize the laptop for work, and even personalized suggestions for digital tools that might align with the user’s emerging interests (like coding or design software). It’s adjusting the recommendations in real-time, based on emerging user patterns and broader trends.

5. Complexity and Computational Power

• AI Agents require less computational power because they operate within a fixed framework. They are efficient for repetitive tasks but lack deep reasoning.
• Agentic AI demands higher computational resources due to real-time analysis, learning, and strategic planning. It mimics problem-solving closer to human cognition.

Let me provide more details with the example

AI Agent in Weather Forecasting:

An AI agent in weather forecasting typically relies on historical data and predefined models to make predictions about the weather. It uses patterns from the past to predict future conditions, such as temperature, precipitation, or wind speed. These agents work with large datasets collected from satellites, weather stations, and sensors to analyze trends and make forecasts for specific locations.

For example:

• The AI agent might look at weather data from the past few years to predict that a specific region will experience a certain level of rainfall during a particular month based on historical trends.
• It could predict that a city will be sunny tomorrow, based on the typical conditions that have occurred on that same day over the past several years.

Agentic AI in Weather Forecasting:

Agentic AI in weather forecasting, on the other hand, is much more dynamic and adaptive. Instead of just relying on historical data, it can consider new variables in real-time and adjust its predictions as circumstances change. This type of AI not only incorporates traditional weather patterns but also dynamically integrates emerging data, new climate trends, and unforeseen variables that could impact weather outcomes.

For example:

• Adapting Forecasts Dynamically: Suppose a storm front is developing unexpectedly in an area where it’s not typically common. An Agentic AI weather system would recognize the change and adjust the forecast accordingly. Instead of sticking to a prediction of clear skies, it would dynamically update the forecast to include warnings about potential rain, wind, or even a sudden shift in temperature.
• Considering New Climate Patterns: If the AI recognizes that the region’s typical weather patterns have been altered due to climate change (e.g., more frequent heatwaves or erratic rainfall), it will take these factors into account and adjust its predictions. It might predict that a summer month will have unusually high temperatures, based on newly observed climate patterns rather than just historical data.
• Incorporating Unforeseen Variables: If a new storm or weather system emerges that wasn’t predicted by traditional models, the Agentic AI can adjust the forecast in real-time. It might recognize patterns in atmospheric pressure, ocean temperatures, or wind currents that other models would miss, allowing it to forecast severe weather events, such as hurricanes or tornadoes, much earlier than conventional methods.

Example of Dynamic Forecasting:

Let’s say, based on historical data, an AI agent forecasts that a city will experience sunny weather tomorrow. However, a cold front begins to move in unexpectedly from the ocean overnight, changing the atmospheric pressure. An Agentic AI would immediately recognize this shift in the weather system and update its forecast, predicting rain or colder temperatures instead of the originally expected sunshine. It might even factor in the speed and intensity of the front, offering more granular predictions like the specific time when rain will begin or the likelihood of a temperature drop.

Key Differences:

• AI Agent in Weather Forecasting: Predicts the weather based on historical patterns and fixed models. It’s reactive and works with data that is relatively static. It provides forecasts based on what has happened in the past, making it less capable of adapting to unexpected or sudden changes in weather.
• Agentic AI in Weather Forecasting: Adapts dynamically to new data and unforeseen variables. It continuously updates its predictions as new information (such as emerging weather systems, changes in climate patterns, or real-time sensor data) becomes available. It can adjust forecasts on the fly, making it more accurate in predicting weather in situations that don’t align with traditional patterns.

Example Comparison:

• AI Agent Example: A traditional weather AI predicts that a city will have sunny weather tomorrow based on historical data for that day. If an unexpected cold front moves in, the prediction might not change, leading to an inaccurate forecast.
• Agentic AI Example: An Agentic AI would predict sunny weather initially but would quickly adjust its forecast if it detects a cold front moving in. It might also predict a 60% chance of rain in the afternoon and update the user on the expected temperature drop, giving them more accurate and relevant information in real-time.

6. Real-World Application Scope

• AI Agents are ideal for controlled environments with structured inputs, such as chatbots, virtual assistants, and automation tools.
• Agentic AI thrives in dynamic environments where decision-making and adaptability are crucial, such as robotics, financial markets, and strategic planning.

Let me provide more details with the example

AI Agent in Email Sorting:

An AI agent in email management is typically designed to handle a specific task, like sorting incoming emails into predefined categories or folders. It works based on rules or patterns, such as keywords, sender addresses, or pre-set folders, to automatically sort emails into different buckets. For example, it might categorize emails into folders like "Work," "Personal," "Promotions," or "Spam."

For instance:

• The AI agent might sort all emails from a specific email address or domain (like your boss or a specific company) into your "Work" folder.
• It might recognize that emails with the word "Sale" in the subject line belong in the "Promotions" folder.
• The agent might flag emails containing suspicious links or unknown senders as "Spam."

Agentic AI in Email Management:

Agentic AI in email management, however, takes the task of sorting and organizing emails to a much higher level of sophistication. It goes beyond simply following a set of predefined rules. An Agentic AI can optimize workflows, prioritize important messages, and even draft replies based on your preferences, tone, and past behavior.

Here’s how Agentic AI would improve email management:

• Optimizing Workflows: The Agentic AI continuously learns how you interact with emails. For instance, it might learn that you usually respond to emails from a specific client or colleague within an hour, while you tend to delay replies to newsletter-type emails. Based on this, it could automatically prioritize certain messages, placing them at the top of your inbox or sending you reminders for those that require a timely response.
• Prioritizing Important Messages: Instead of just sorting emails based on keywords, the Agentic AI would analyze the context of each message and prioritize emails based on importance. For example, it could recognize that an email from a colleague with the subject "Project Deadline Tomorrow" is more urgent than an email from a mailing list, even if the latter has a keyword like "important" in the subject line. It might flag the project email as "High Priority" and even send you a reminder.
• Drafting Replies Based on User Preferences: Over time, the Agentic AI could also learn your style of writing and draft email replies on your behalf. For instance, if you tend to keep your replies short and professional, it will start suggesting short and to-the-point responses to similar emails. Or, if you regularly use certain phrases (e.g., "Thanks for the update, I’ll review it and get back to you"), the AI might automatically suggest those phrases when responding to similar messages. The more it learns about your preferences, the better its drafts will become.
• Dynamic Adjustments Based on Context: Agentic AI can adjust its behavior based on changing contexts. If you’re about to leave for vacation, the system could detect that you’re not checking emails frequently and start automatically responding with a message like, "I’m currently out of the office, and will get back to you upon my return." If an email needs urgent attention, it could bring it to your attention, even if it was normally categorized under lower-priority tasks.
• Learning from Feedback: One of the most powerful features of Agentic AI is its ability to learn from feedback. If you consistently move certain types of emails from one folder to another, it will adjust its sorting algorithm over time. If you often mark certain emails as spam or flag specific contacts as important, the Agentic AI will refine its filtering and prioritization process, becoming more intuitive with each interaction.

Example of Dynamic Workflow Optimization:

Let’s say you’re in the middle of a busy week at work. Your Agentic AI could help you by:

1. Sorting incoming emails based on importance, ensuring that work-related messages (especially time-sensitive ones) appear at the top of your inbox.
2. Prioritizing emails from your manager or key clients by marking them as urgent, so you don’t miss anything critical.
3. Analyzing a few incoming messages to determine if they’re likely to need a follow-up. If the AI determines that a particular email requires an immediate response, it could suggest a draft reply that matches your tone and previous responses.
4. Automatically flagging routine emails (e.g., daily status updates) as lower priority, so they don’t clutter your focus.
5. As you finish reading and replying to your emails, the AI could even offer suggestions for improving your email management in the future, based on your habits.

Key Differences:

• AI Agent in Email Management: Sorts emails into predefined folders and categories based on specific rules or keywords. It’s reactive and static, only responding to rules that have been set for the system. It doesn't adapt to new priorities or proactively suggest actions like replies or reminders.
• Agentic AI in Email Management: Optimizes workflows dynamically, prioritizes urgent messages, and adapts to the user's changing needs and preferences. It actively analyzes the content, context, and importance of incoming emails, learns from previous interactions, and can even draft replies or manage tasks on your behalf.

Example Comparison:

• AI Agent Example: The AI agent might sort emails from your boss into a "Work" folder and categorize a marketing email into a "Promotions" folder. However, both types of emails would be treated the same without considering urgency or context.
• Agentic AI Example: The Agentic AI would sort the email from your boss into a "High Priority" category and ensure that you see it first. It would also draft a response to a marketing email based on your usual tone and preferences, or it might even suggest that you unsubscribe if it detects that you never engage with such emails.

Key Differences Between Agentic AI and AI Agents