Intro

In the first installment of our AI Agents series, we dove deep into the fascinating topic of open-endedness, which is highly relevant to the development of autonomous agents.

While we’ve explored that core concept, we haven’t yet tackled the many definitions surrounding AI agents or the misconceptions that come with them. Some call them "bots," others "agents," and there are even more terms in use. Is this okay? Yes and no. To truly make sense of the pivotal changes AI agents are bringing, and to effectively communicate about building systems around and with them, we need to understand why these differences matter. Grouping similar concepts together will help us feel more comfortable with the language, clarify distinctions, and address common misconceptions. Think of this as your go-to vocabulary for navigating the complex world of AI agents and workflows!

In today’s episode:

Core Agentic Concepts

The term "AI agent" serves as the central concept – an umbrella term – that unifies the entire discussion of agentic behavior and functionality in AI systems. At the heart of AI agents are the ideas of autonomy, perception, decision-making, and action, which manifest differently depending on the complexity and purpose of the agent. To implement these capabilities effectively, AI agents rely on key modules: profiling, which assigns roles to guide behavior; memory, allowing agents to retain and reuse information; knowledge, enabling them to start with domain-specific expertise; reasoning/planning, to break down tasks and orchestrate steps; and actions, which integrate external tools to achieve their goals.

These components (which we will expand on in the upcoming episodes) form the building blocks for creating capable and intelligent autonomous agents. This leads us to the concepts of Autonomous, Intelligent, and Rational Agents.