HubSpot’s Prospective AI Agent Platform Races Past 500,000 Users

5,000+ people have also leveraged the platform's low-code tool to build custom agents

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HubSpot’s Prospective AI Agent Platform Races Past 500,000 Users
CRMLatest News

Published: February 3, 2025

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Floyd March

Agent.ai, the agentic AI platform built by HubSpot Founder & current CTO Dharmesh Shah, has surged past 500,000 users. 

The platform only had 250,000 users at the turn of the year, highlighting its rapid growth in January.

Taking to LinkedIn to express his excitement, Shah said: “Woo hoo! Agent.ai now has 500,000+ users. This is about 10X growth in 6 months.”

The Founder thanked users who have tried out the agents to date and expressed gratitude to those who have submitted one of the 13,000 ratings and reviews, as it “helps the best agents be more easily discovered.”

Also, deep gratitude to the 5,000+ people who have used our low-code tool for building an agent.

Currently, Agent.ai runs independently from HubSpot. Yet, Shah hopes that the platform will – ultimately – become part of the CRM giant’s core offering.

There is history of this, with Shah’s previous pet project – “ChatSpot” – now flying under the HubSpot banner as “Breeze Copilot”.

From a September 2024 Launch to 500,000 Users in Less Than a Year

Shah launched Agent.ai during HubSpot’s INBOUND 2024 event as a network of autonomous AI agents.

These agents would cover various use cases – such as service, sales, and marketing – while collaborating to automate multi-step processes. 

Users can scout these agents on the network and test them within their CX ecosystem before deploying and optimizing them.  

By January, the platform had already surpassed Shah’s hopes of having 100,000 platform users. Now, it has five times that number. 

Most of these users will have scoured the network for those preconfigured agents. However, Agent.ai also offers a low-code interface for CX teams wanting to create their own custom-built agents. 

These AI agents can be shared with the broader community and given reviews of one to five stars. 

As of January, the average rating for public AI Agents is 3.99 stars, which Shah views as a positive sign for an early-stage platform. 

However, he acknowledges that there’s still room for improvement in the quality of the AI agents and mentions that he’s working seven days a week to continue enhancing the offering.

What Are AI Agents?

Despite the high interest in agentic AI, it’s still a relatively new concept for many customer experience leaders.

As such, it’s critical to get to grips with what they actually do.

In short,  AI Agents are powerful, proactive, and autonomous bots designed to take action and accomplish goals without requiring constant user input.

They represent a significant step toward making conversational systems more independent problem solvers or assistants.

Built on advanced AI, these agents possess autonomy, reasoning, and planning capabilities. Their flexibility allows them to be dynamic and proactive while also maintaining deep contextual understanding and foresight.

Moreover, AI Agents are goal-oriented and can make decisions independently. In many instances, they can interact with and operate across multiple systems.

Use Cases For AI Agents In Customer Experience

There are many use cases for AI Agents across multiple sectors, all of which can directly impact customer experience. 

For example, in retail, post-purchase assistance is one of the more significant use cases for agentic AI. After all, an AI agent can automatically send setup instructions to new customers or offer accessories for their purchase.

In healthcare, consider use cases such as appointment management, where an AI agent may streamline scheduling, reduce missed appointments, and save time and costs. 

Banking and wider financial services is also a fascinating sector to look out for. Top of the list for use cases is fraud prevention, as AI Agents can detect unusual activity on a customer’s account, temporarily freeze it, and notify the customer with steps to confirm or dispute the transaction. That’s an excellent example of AI agents working as a team. 

One final sector to consider is education and learning services. Here an AI agent can recommend tailored study plans, extra resources, or skill-building courses.

Yet, there are many more examples in these and other sectors, both in customer experience and the broader enterprise.

 

AI AgentsArtificial IntelligenceCRM

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