What is Agentic AI?
Agentic AI is the next step beyond AI models aka Large Language Models (LLMs), as it can carry out given tasks on its own by planning, reasoning, and executing logical steps. It does not need human intervention at each step, which is why Agentic AI is also referred to as autonomous AI models. This is what makes them different from your typical AI chatbots like ChatGPT or Google Gemini, which merely responds to your text prompts with no sense of agency. Agentic AI can execute tasks like responding to an email for you, checking bugs and fixing them, or planning a trip and buying tickets for you. All without any human help, which makes them so useful.
How Does Agentic AI Work?
Unlike your everyday AI (Artificial Intelligence) models, Agentic AI models are trained to perform tasks, which helps them do problem-solving, and carry out task independently on their own. Here is how these next generation models work.
Reasoning: Agentic AI uses large language models to reason, understand the objective, break it down into smaller steps and chart the course of actions it needs to take. Memory and Learning: It remembers what it has done from memory and uses that information to make real time decisions for future actions. Taking Action: Finally it takes the action, adapting to its environment and uses external tools to complete its goal.
What are the Different Types of Agentic AI Models?
Now, we have learned what is Agentic AI, but did you know there are different types of AI agents. These range from simpler agentic AI system, to more complex ones depending on the kind of task they need to carry out. So here is a quick breakdown of all the types of Agentic AI systems.
Simple Reflex Agent System: These are the simplest form of AI agents that perform an action when a certain condition is met. Like automatically sending email on a particular date or time. Model Based Reflex System: This model creates an internal memory, that it uses to complete a task. This memory is constantly updated to predict its next course of action.
Goal Based System: Goal based agentic AI models work independentaly to understand, learn and predict future actions to reach its goal. Similar to how a robotic vaccum will learn its surroundings and find the best path to clean the floor efficiently. Learning Agents: Similar to Model based reflex agents, learning AI models use the past data to improve their efficiency. These models have a “critic” component which tells them how well they are performing a given task. Heirarchal Agents: This Agentic AI model breaks down a task into a multi-step plan of action, making it easier for it to understand and achieve its goal. Multi-Agent Systems: This is used for more complex situations. Here, multiple specialized agents collaborate with each other to accomplish a set goal with evolving challenges.
What is the Difference Between Agentic AI and Generative AI?
Both Agentic AI and Generative AI are powered by advanced AI models, but they serve completely different roles. Let me explain with the help of a table.
What are Some Common Applications of Agentic AI?
These days, Agentic AI models have become quite common in areas like web browsing, deep research, and even in other areas like manufacturing and self-driving cars. But let me give you a few everyday applications of Agentic AI where you can use and see them in action. This brings us to the end of this crash course on agentic AI model. This technology is just in its infancy and over time, we will start to see a more wider use for it in everyday things. The best part is, we might not even know that it is there since it will be working autonomously. In case I missed out on anything then do let me know in the comments section down below. Name Email ID
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