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:

An AI Agent doesn’t just execute tasks—it analyzes, learns, and refines its strategy over time, making it a dynamic problem-solver rather than a static and rigid executor.

This ability to evolve enables AI Agents to handle unforeseen challenges, adjust to changing conditions, and optimize their approach based on past experiences. Unlike traditional automation, which requires manual reprogramming to adapt, AI Agents independently refine their decision-making processes, ensuring more effective and efficient performance over time.

At HubSpot, an agents ability to learn and grow is particularly advantageous for our customers. The HubSpot Framework gives our AI Agents a more holistic view of the customer lifecycle, more data in means better signals for our Agents to learn and evolve from.

The Core Components of an AI Agent

At the centre of every AI Agent is a Large Language Model (LLM). Most commonly the LLM of choice is GPT-4o but the number of alternatives continues to increase include proprietary foundation models such as Anthropic's Claude or AWS's Nova models along with open source competitors such as Meta's Llama, Mistral and indeed DeepSeek's R1. These foundation models are trained using different different algorithms such as Reinforcement Learning with Human Feedback (RLHF) or instruction fine-tuning to name a few.

AI Agents have four primary components: Autonomy, Skills/Tools, Memory/Context and a Feedback loop. In addition, Agents are stateful, they remember, they learn and they adapt.

1. Autonomy – AI Agents make decisions without human intervention, adjusting their approach as needed.
2. Skills & Tools – Agents utilize a library of skills, from retrieving data to performing complex operations.
3. Memory & Context Awareness – Agents are stateful and instances remember past interactions and incorporate context.
4. Feedback Loop – AI Agents evolve based on explicit user feedback and implicit learning mechanisms.

Context Awareness can seem ambiguous for AI Agents context is often provided to the Agent through Retrieval Augmented Generation (RAG), storing previous conversations and allowing the agent to learn from past user preferences. The feedback loop is a vital component to allowing Agents to learn via RLHF or Direct Preference Optimisation.

Examples of AI Agents in Action

Some common examples of agents today are customer support agents and sales automation agents. Both types were part of our Inbound product launch where we release the Breeze Customer Agent and the Breeze Prospecting Agent. The Customer Agent helps deflect Customer Support queries based on a portals Knowledge Base. The Prospecting Agent generates personalised outreach and can be configured to run in autonomous or semi-autonomous modes.

• Customer Support Agents: AI-powered assistants that not only answer FAQs but also refine their responses based on customer interactions.
• Sales Automation Agents: Systems that adapt outreach strategies based on customer engagement.

Why AI Agents Matter

Traditional automation is efficient, but it lacks flexibility. AI Agents offer:

• Greater efficiency by reducing manual oversight.
• Better decision-making by continuously improving from feedback.
• Scalability as they handle complex workflows without human intervention.

As AI Agents move up the continuum and become more complex and autonomous they'll provide greater utility to business and augment the productivity of existing employees.

What’s Next?

• AI Skills: The Agentic Building Blocks
• How HubSpot’s AI Team is shaping the future of AI Agents.
• The transition from single to multi-agent architectures.

• In March, we'll dive deeper into some of the technical concepts surrounding Agents and Generative AI.

AI Agents are not just smarter automation—they are adaptive, evolving systems that redefine how we interact with technology.