Machine Learning & AI

Posts about Breeze AI:

The importance of tools in developing ai agents

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Gareth Austen
AI Breeze AI AI Agents
In my last post, I talked about what an AI Agent is and how it learns and reasons about it's environment to make effective decisions. At the core of an Agent is a Large Language Model (LLM), these LLMs contain a vast amount of knowledge but this knowledge is limited in recency up to the last date that data was gathered for training. If, for example, we want an agent to conduct company research on to prepare a sales rep for an upcoming pitch then LLMs are unlikely to have the latest company news contained in their training data. How do Agents get around this shortcoming... by using tools to query the web, interact external APIs and much more.

What is an AI Agent

Picture of Gareth Austen
Gareth Austen
Breeze AI AI Agents

At HubSpot we’re investing in AI Agents and an Agent Platform in 2025. We launched our initial 4 Agents at our 2024 Inbound conference which were very well received by our customers. In 2025, we want to make it easier and faster to launch agents but this meant answering some fundamental questions about agents. The first of which is What is an AI Agent?

What is an AI Agent?

The term AI Agent has become a buzzword, often used interchangeably with automation. But not all automation is an AI Agent. In this article, I’ll clarify what an AI Agent is, how it differs from traditional automation, and why it represents a major shift in how businesses leverage AI.

Defining an AI Agent

At its core, an AI Agent is a goal-oriented, autonomous system that can make decisions, adapt to new information, and continuously improve its execution. Unlike traditional automation, which follows a predefined set of rules, AI Agents have context awareness, adaptability, and autonomy. With that said, AI Agents exist on a continuum and have varying levels of complexity and autonomy. 

Here’s how they differ: