Decoding Intelligent Agents: Exploring Agent Programs and the Rise of Agent Experience (AX)

Author(s): Suresh D

Originally published on Towards AI.

Source: Image created by the author using an image generation tool.

Introduction

Imagine Tara wants to find the best deal on a new coffee machine. Instead of spending hours browsing multiple websites, comparing specifications, and reading countless reviews, she instructs her AI agent. This agent autonomously navigates various online retailers, analyses pricing structures, examines customer feedback, and considers Tara’s preferences like brand, features, and budget. Within minutes, the agent presents a concise summary of the top three deals, explaining why each is a good option. This seamless interaction underscores the rising importance of Agent Experience (AX), which is critical as businesses across industries increasingly rely on AI agents.

Definition of Intelligent Agents

To understand AX, we must first define what constitutes an AI agent. According to Stuart Russell and Peter Norvig in “Artificial Intelligence: A Modern Approach,”

An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.

Figure 1 Agents interact with environment through sensors and actuators (adapted from [1])

The interaction between an agent and its environment is a fundamental concept in artificial intelligence, as illustrated in Figure 1. The central box with a question mark symbolizes the agent’s internal decision-making process. Environment surrounding the… Read the full blog for free on Medium.

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Published via Towards AI