Agentic AI and computer vision: The future of automation

Artificial intelligence (AI) and computer vision help machines see and understand the world. Thanks to recent advancements, we are now witnessing a leap forward - AI innovations that not only perceive but also think, plan, and act on their own. In a previous article, we discussed how Vision agents are able to process visual data, analyze it, and take action. 

Today, we’ll explore a similar concept: agentic AI. Agentic AI systems are designed to operate independently and have human-like reasoning and problem-solving abilities to achieve defined goals. Unlike traditional AI systems, which focus on completing individual tasks with predefined instructions, agentic AI can plan and act autonomously to perform tasks. These agents can even learn from previous interactions and execute decisions without any human intervention. 

When it comes to computer vision, agentic AI systems can leverage techniques like object detection using computer vision models like Ultralytics YOLO11 to analyze visual data in real-time, recognize objects, understand spatial relationships, and make autonomous decisions based on their environment.

What is agentic AI?

At its core, agentic AI systems are designed with autonomous, goal-oriented thinking, adaptive problem-solving, and continuous learning abilities. They use AI agents to understand their environment, make decisions, and execute tasks. These AI agents use computer vision models, reinforcement learning techniques, and large language models (LLMs) to perform complex tasks. This makes them ideal for automating business workflows and enhancing decision-making.

For example, in a warehouse, an agentic AI system equipped with computer vision can detect packages, track inventory, and navigate around obstacles without human intervention. Using reinforcement learning, it can improve its movement efficiency over time, learning the best routes to avoid congestion. Meanwhile, an LLM-powered chatbot can assist workers by answering queries and suggesting operational improvements, making the entire workflow more efficient.

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The key difference between a traditional AI solution and an agentic AI solution is that agentic AI can think ahead and adapt to changing situations. Traditional computer vision systems are great for recognizing objects or classifying images, but they can’t adjust their behavior dynamically. They need a human to step in and help retrain or fine-tune models. Meanwhile, agentic AI uses advanced machine learning techniques to improve over time by interacting with its environment.

Comparing agentic AI to other advanced AI innovations

AI is evolving fast, with new concepts like generative AI, agentic automation, and computer vision being rapidly adopted across various industries. Let’s compare these technologies to understand better what sets agentic AI apart.

The difference between generative AI and agentic AI

If you’ve used tools like ChatGPT, you’re already familiar with generative AI. This branch of AI specializes in creating content, such as text, images, or code, based on user prompts. While generative AI enhances creativity and idea exploration, it follows learned patterns and operates within predefined constraints, lacking the ability to make autonomous decisions or pursue independent goals.

In contrast, Agentic AI actively pursues goals. It can adapt dynamically to its environment without requiring continuous human input. Instead of merely generating content, it takes action and solves problems autonomously.

Agentic automation and agentic AI are closely related

Agentic automation and agentic AI go hand in hand, with agentic AI providing the intelligence that powers automation. Consider a computer vision-based security system. 

The agentic AI system analyzes the situation, decides the best response, and takes action on its own. For example, if an AI security camera integrated with computer vision spots an intruder, the agentic AI system doesn’t just send an alert; it checks if the person is an employee, locks doors if needed, tracks their movement, and even sends a drone to monitor them.

Agentic automation makes sure all these actions work together smoothly. It connects different systems, like security cameras, door locks, and drones, so they can respond automatically and in sync. While agentic AI makes the decisions, agentic automation ensures those decisions are carried out efficiently without needing human intervention. 

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How agentic AI works

Now that we’ve a better understanding of what agentic AI is, let’s explore how it works. 

Agentic AI systems operate through a cyclical process of perception, decision-making, action, and adaptation, helping them to learn and improve over time. This continuous loop allows these systems to function on their own and achieve complex goals.

Here’s a quick look at the steps involved in the continuous loop:

• Perception: The agentic AI system collects and analyzes data from cameras, sensors, and user interactions to better understand its surroundings.
• Decision-making: The system evaluates different options, predicts possible outcomes, and selects the best action based on reasoning and risk assessment.
• Action: Once a decision is made, the system executes tasks by controlling physical devices, interacting with other systems, or generating outputs.
• Adaptation: The system learns from experience using feedback, applying machine learning and reinforcement learning to improve performance over time, especially on more complex tasks.

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Real-world applications of agentic AI

Next, let’s walk through some real-world examples of agentic AI in action. These systems are being used across different industries, helping machines analyze data and make independent decisions to improve results.

Agentic AI in drug discovery

Drug discovery involves several key stages, from identifying biological targets linked to diseases to screening potential compounds, optimizing their chemical structures, and conducting preclinical testing. It is a complex and time-consuming process that requires extensive data analysis and experimentation to find effective and safe treatments.

Agentic AI, integrated with computer vision, is helping automate key steps like chemical synthesis, making the process faster and more efficient. Chemical synthesis is the process of combining different chemical compounds to create new substances, such as pharmaceutical drugs, through controlled reactions. Traditionally, scientists had to manually adjust factors like temperature, solvent composition, and crystallization timing through trial and error.

Now, agentic AI systems can monitor reactions in real-time, analyze visual changes such as color shifts or crystal formation, and make decisions on the spot. For instance, if the system detects that a reaction is not progressing as expected, it can immediately adjust the temperature or add the necessary chemicals to optimize the process. By continuously learning from past reactions, the system improves its accuracy over time, reducing the need for manual intervention and speeding up drug development.

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Reinventing e-commerce with agentic AI

Agentic AI is changing the way we shop online by making the experience more personalized, efficient, and automated. Instead of just recommending products based on past purchases, agentic AI can analyze browsing habits, predict what a customer might want next, and adjust product suggestions in real-time. 

With the help of computer vision, agentic AI can also analyze visual searches, recognizing product images to offer more accurate recommendations. For example, if someone frequently looks at sneakers, the agentic AI system can highlight trending styles, offer discounts, or suggest matching accessories. It can also optimize pricing and promotions based on demand, making shopping more dynamic.

Beyond recommendations, agentic AI is improving e-commerce logistics by managing inventory, predicting restocks, and automating order fulfillment. Computer vision allows agentic AI systems to track stock levels in real-time, identify misplaced items, and ensure products are correctly categorized. If an item is selling out quickly, the system can trigger restocking or suggest alternatives. By learning and adapting over time, agentic AI is making online shopping faster, smarter, and more seamless for both customers and businesses.

How to build an agentic AI system 

Now that we’ve looked at real-world examples of agentic AI, let’s discuss how to build one. 

If you're developing a computer vision-based application, using the latest models like Ultralytics YOLO11 can help your agentic AI system better understand its surroundings. With its support for various computer vision tasks, YOLO11 can make it possible for agentic AI systems to analyze visual data accurately.

Here's how you can build an agentic AI system using YOLO11:

• Define objectives: Clearly outline the AI agent’s purpose, goals, and the specific tasks it needs to perform to achieve its intended functionality.

• Train YOLO11: Collect relevant image and video data, label it, and custom-train YOLO11 based on your specific application.

• Integrate YOLO11: Connect YOLO11 with an AI framework that enables real-time analysis and decision-making based on detected visual data.

• Enable autonomous decision-making: Set up logic or machine learning models that allow the AI agent to take actions based on YOLO11's detections, such as triggering alerts, adjusting settings, or guiding robotic systems.

• Incorporate feedback loops: Implement a self-learning system where YOLO11 refines its accuracy by retraining with new data, improving its model performance over time.

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Pros and cons of an agentic AI system

Here are some of the key benefits that agentic AI systems can bring to various industries:

• Increased efficiency: Agentic AI systems can automate complex, time-consuming tasks, reducing errors and freeing up human workers for higher-value work.

• Scalability: These systems can easily adapt to different industries and grow to handle larger workloads as needed.

• Cost reduction: By reducing the need for manual labor and optimizing operations, agentic AI helps businesses cut expenses and use resources more effectively.

While agentic AI offers many benefits across different sectors, it's also important to be aware of the potential limitations that come with it. Here are some key concerns to keep in mind:

• Bias in AI: Agentic AI systems can inherit biases from training data, leading to unfair or inaccurate outcomes, especially in areas like hiring and law enforcement.

• Lack of transparency: Many AI models work like "black boxes," making it difficult to understand how they make decisions, which can be a problem in industries like healthcare and finance.

• Regulatory challenges: Agentic AI development is moving faster than regulations, creating legal uncertainties and inconsistent global compliance standards.

Overall, while agentic AI systems have a lot to offer, it's important to balance their benefits with ethical considerations, transparency, and proper regulation to ensure they’re used responsibly.

Key takeaways

When combined with Vision AI models like YOLO11, agentic AI systems can change the way automation works. From self-driving cars to online shopping and healthcare, these systems help businesses work autonomously and at a faster rate. 

However, challenges like bias, lack of transparency, and unclear regulations still need to be addressed. As agentic AI systems improve, finding the right balance between innovation and responsibility will be key to making the most of these innovations.

Join our community and GitHub repository to learn more about AI. Explore various applications of AI in manufacturing and computer vision in healthcare on our solution pages. Check out our Ultralytics YOLO licenses to get started with computer vision today!