Guiding Questions:
-What makes Agentic AI different from other types of AI?
-What is the main goal of an AI agent’s architecture?
-Why do we consider Agentic AI to be described as a cycle rather than a single process?
-What makes an AI system inherently “Agentic” instead of being reactive or just used once?
Understanding how AI agents work can seem complex, but it’s really about breaking down their core components. At its heart, an agent architecture considers several independent functions that work together to enable an agent to perform its tasks. These agents operate using knowledge and a defined purpose. A fundamental aspect is how these components interact to process information and make decisions. We often see patterns emerge from these interactions, shaping the agent’s behavior. The architecture also involves the way an agent perceives its environment and how it then acts upon that understanding. Thinking about agents, it is important to remember that a good design can greatly improve their reliability and their ability to adapt. These agents represent a step forward in how we design software that can respond to dynamic situations. Agent architecture looks at how an agent system is generally set up and how information moves through it. This covers everything from how goals are understood, to the decision-making process, how actions are carried out, and how results inform what happens next.Thinking about the larger structure helps us see that Agentic AI isn’t just one process. Instead, it’s a constant cycle of how it senses things, figures them out, and then acts. Many systems that act independently tend to go through a similar cycle. The agent starts when it gets some input, like a user asking for something, an event on the schedule, or a signal from another system. The system takes in information, understands it based on its current goal, plans what to do next, carries out those steps, checks the outcomes, and then considers if those actions helped it get closer to its full objective. This cycle repeats until we reach the goal or someone tells us to stop. Inside this cycle, you can spot several different pieces. A reasoning component helps understand goals and figure out what to do next. A memory component keeps important information for later. A tool, or what we sometimes call an action layer, helps an agent engage with systems outside its own programming. A control layer brings all of these parts together, making sure things happen in the right order within the given limits. These parts work together, allowing the system to run without interruption instead of just activating one time. It’s worth noting something important about agent architecture: it centers on keeping different parts of a system separate. When we look at how things work, we see that making a decision, storing memories, and taking action are all considered separate jobs. Modularity helps with agentic systems because it makes them simpler to comprehend. It also allows for easier expansion and management, particularly when these systems become more intricate, which is a key advantage.
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