From the course: Agentic AI Fundamentals: Architectures, Frameworks, and Applications
How AI agents make decisions
From the course: Agentic AI Fundamentals: Architectures, Frameworks, and Applications
How AI agents make decisions
- Agentic AI systems make complex decisions and achieve goals on their own, independent of human interaction and intervention. And with that exciting benefit comes the necessity for careful planning. Your effort spent planning is the difference between an agentic AI system adding value to the business or it being a total failure, even doing damage. If you are tasked with designing and developing ag agentic AI systems, understand that the agentic AI frameworks support AI's planning and problem solving capabilities. First though, let's keep it high level to understand how these capabilities interrelate. We'll start with goal definition, which just means clearly defining objectives the AI needs to accomplish. These should be done before you deploy the system. Goals can be simple tasks like finding moving targets in front of our camera or navigating to a specific location if our agentic AI system is an autonomous car. Or complex, multi-step processes, such as formulating an action strategy based on some event occurring, such as our autonomous vehicle is in an accident, and taking actions to deal with possible injuries and interacting with first responders. Once a goal is determined, so must be the sequence of actions that leads an agent to that goal state. For instance, in our Agentic AI-powered security camera examples, we would like the camera to detect crime and take predefined actions if a crime is observed. So the AI system needs to know how crime is detected, how it's validated, how risks are considered, and how actions are carried out, such as alerting the operator or local authorities. This could look like, number one, observe the yard. Number two, detect humans. Number three, analyze humans. Number four, determine the intention of the humans. Number five, determine risk. Number six, determine trade-offs. Number seven, take predefined actions. I bet you can think of a few ways to plan your agentic AI system in terms of sequences of events that need to occur. Now let's move to environmental modeling, which means creating a representation of the environment in which the AI operates. This means that the agentic AI system replicates a physical environment as a digital one, so that simulations can occur to determine the best outcomes. In our camera use case, the ability to replicate the yard, its monitoring within the camera, allows for simulations such as modeling potential movements, et cetera. Of course, planning is never done. We'll also need to drive a continual planning process. This means updating and revising plans based on new information and changes in the environment. For instance, in our security camera use case, items in the yard such as a bench could be moved, and thus, the camera needs to reevaluate and replan around the changes that have occurred. Different portions of the image now have to be dealt with differently, and actions need to be redone around the changes. As the saying goes, you plan or you plan to fail. In the next video, we'll learn more about autonomous agents, including what they are, how they work, and how they are built by people like you.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.