Part 1: What is an AI Agent? | Evaluating AI Agents with Arize AI

Agentic AI systems are rapidly transforming how we approach problem-solving in artificial intelligence. In Part 1 of our community series with Arize AI, we lay the groundwork for understanding what is an AI agent, its architectures, and design patterns — setting the stage for building smarter, more adaptive systems.

Explore the core building blocks of AI agents, from memory and planning to tool-use and role specialization. Learn the differences between single-agent and multi-agent setups, and take a guided tour of today’s most popular frameworks like LangGraph, AutoGen, and Crew AI. Through real-world examples and interactive tracing demos using Arize Phoenix, you will gain practical insight into how to build and debug agents effectively.

What we will cover:

• Understand what is an AI agent and what makes an AI system an “agent” — and the essential components involved, including memory, planning, and tool use.
• Learn the differences between single-agent and multi-agent systems and when to use each.
• Explore what is an AI agent in various architectures like Router-Tool, ReAct, hierarchical, and swarm-based setups.
• Dive into real-world design patterns and use cases, such as task routing, tool chaining, and role specialization.
• Uncover common failure modes like infinite loops, brittle planning, and tool misuse — and how to spot them early.
• Preview the need for better evaluation methods in agentic systems and why tracing and observability are crucial.
• Watch a live walkthrough of a simple agent trace using Arize Phoenix to understand what is an AI agent and how evaluation and debugging works in practice.

Want to join Part 2 of the series? Find it here!