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Agentic System Design Crash Course

What Makes a System Agentic

Explore the fundamental characteristics of AI agents, including their ability to perceive environments, reason through complex decisions, and act autonomously. This lesson clarifies how agentic systems differ from passive AI models by using real-world analogies and explains when building an agent is beneficial for dynamic, context-dependent tasks.

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An AI agent refers to a system that can perceive its environment, make decisions, and act autonomously to achieve specific goals. This concept, often summarized as ‘perceive-reason-act,’ is a cornerstone of classical AI theory, notably popularized by Russell and Norvig. Unlike a passive model that requires a user to query it or interpret its outputs, an AI agent is an active entity. It can sense, think, and act on its own, often without continuous human oversight.

Let’s break this down more concretely. At its core, an AI agent has three fundamental capabilities:

Breaking down how an AI agent works: It perceives the environment (input), reasons to make a decision (processing), and performs a task (output)
Breaking down how an AI agent works: It perceives the environment (input), reasons to make a decision (processing), and performs a task (output)

  1. Perception: The agent must be able to sense its environment. This could mean reading text from a user, analyzing images or audio, or retrieving data from sensors or databases. The goal is to extract meaningful information from the raw input.

  2. Reasoning and planning: Once the environment is perceived, the agent must make decisions. This involves understanding context, selecting actions, and planning steps toward a goal. LLMs like GPT-4 are often used here, providing powerful language-based reasoning abilities. Agents can be reactive, responding directly to immediate stimuli, or deliberative, engaging ...