I've been in the private beta of Breeze Studio custom agents since June 2025, with a small group of partners on HubSpot's AI Partner Advisory Council. So I've watched it get built from the ground up. It's been quite a journey to see it grow, and one of the main ways I grew into AI myself.
Now I can finally share what I've learned, and what you can actually do with the agentic side of HubSpot.
In this article:
Every custom agent you run in HubSpot now costs credits. You can still test and run simulations for free, but a live agent counts against your credit meter and cap. The costs shown are estimates — informational, and they vary from the real run.
Set your run limits before you switch anything on. That's the first thing I'd do today if you are diving straight in.
Two big functions, wired together:
1. Custom Agents. A custom agent is a configurable AI worker. You give it:
These workers handle recurring tasks for you, like a very capable intern would — as long as you give them the right "employee onboarding".
2. The Agent Automation Builder — Agentic Workflows.
This is the automation around the worker, or workers. You already had automation and workflows for years, but now we can actually chain several AI Agents together for full workflow/process automation. If you already build workflows, either in HubSpot, N8N or Make or Zapier, you will already know how this works.
How it works?
An Agent is basically a workflow step. So is an ad-hoc AI step you write as either a small prompt, or a very elaborate AI Agent. In addition, you can call upon external LLMs if you want to outsource work out of Hubspot. Currently those models are by: Anthropic, OpenAI, Cohere, Gemini or Grok
This is one of the first questions I get, and it's fair. The short answer to the question?: An AI assistant helps you, once. An AI agent does the work (potentially) without you, every time.
Here's a comparison in a bit more detail between what AI Assistants do vs AI Agents vs traditional workflow automation:
But there's more to it. Here's roughly where B2B commercial teams are right now:
What does this mean? That most teams also lack experience in building scalable AI solutions. Part of the reason is that most of us are still "zero-shot prompters".
A zero-shot prompt asks the model to do something without giving it examples, context or a format — "write a blog post about X," "summarise this call." Whatever you leave out, the model guesses.
This is fine for a single task, but if you're looking for a highly scalable commercial process where you need stability, repeatability and dependability this falls apart:
An AI agent has the same judgement every time, is set up once and applied the same way every time it runs. It's the same model with the prompt written properly once — role, goal, method, output format — with your brand kit, ICP, way of working, standard operating procedures, buyer persona's, and product catalogue behind it as context. Instead of hoping every rep prompts well, you set it up one time.
So the rule I use with customers:
Being honest, after thirteen months in it:
None of this is a reason to wait. It's a reason to scope your first build around what's live today, and to set run limits before the meter starts.
Here would be my dreamsheet for improvements to the functionality (and I shared this with HubSpot's Product team). Some are already on the roadmap:
A quick filter before either example. If HubSpot already ships an out-of-the-box agent or AI feature for the job, use that — don't build your own. Pre-call research, call recaps, deal scoring, lead routing, generic quote generation, RFP responses: those already exist. Rebuilding them is wasted effort.
A custom agent is worth it when the job needs your proprietary knowledge — your catalogue, your engineering rules, your pricing, your delivery method, your rate card. That's what a stock agent can't have. Both examples below are chosen to sit outside what's already in the box.
Let's make it tangible, because there's enough solutions, but not always the right fit problem or use case.
Take a manufactuer of complete food-processing lines. An RFQ here isn't a line-item order — it's a functional spec, sometimes a full tender: throughput, product characteristics, hygiene and ATEX constraints, footprint, plant integration. It arrives as a long PDF and waits for an application engineer to find time to read it, judge the fit, and start a response. Serious tenders sit cost a lot of time to sift through, judge, and create a credible first analysis or response that is often what wins a place on the shortlist.
Almost everything is engineered-to-order, and a machine-made price is a machine-made mistake. So you don't create an AI agent to create a quote, but rather to save time on qualification.
Here's what an Agentic Workflow could look like in HubSpot:
A lot of prework is done by the time the engineer picks up a qualified structured tender - reference cases attached, open questions listed, feasibility flagged.
Example ingredients for your custom agents:
Inputs
Outputs:
Here's what Tender Agent as an example could look like:
Now let's take the example of a software implementation partner — the kind of firm that puts SAP, Oracle or Microsoft Dynamics into a mid-market business. A prospect sends an RFP: modules wanted, integrations, a data-migration, user counts, a go-live date. Today a solution architect reads it, maps it to their toolkit, estimates effort, checks who's free, prices it, and drafts a proposal — usually over a couple of evenings, because it competes with billable work.
The same RFP now triggers an Agentic workflow.
What lands on the architect's desk is a first-draft SOW that already looks like your firm's work: scoped your way, estimated against real benchmarks, priced on the real rate card, staffed with real names. Low-margin or high-risk deals are flagged for a partner first. The architect edits and pressure-tests the judgement calls — they don't start from a blank page at 21:00.
Example ingredients for your custom agents:
Inputs
Outputs:
Both stories run the same architecture: one agentic workflow, a handful of specialised custom agents doing consecutive work, each grounded in a knowledge vault only you can provide, all under one control plane. Several agents, each one performing one key task, on one workflow.
And here's what that Requirements Reader Agent could look like:
[END CHAPTER HERE WITH TAKEAWAYS]
The order that actually works. Notice the tooling comes last, not first:
In teams that do this well, steps 1–4 take longer than the build itself. The failures I've seen all skipped straight to "let's build an agent".
Most commercial use cases are still better solved with clean data and plain automation. Agents belong on the twenty percent that genuinely needs to read between the lines.
What changed today is that this now runs in the system your commercial team already opens every morning, where previously there would be friction in enabling CRM users to themselves launch AI agents on demand.
After thirteen months of building these, the conversation I still find most useful is the unglamorous one: working out where an agent actually fits in a commercial process, and where it doesn't.
That's exactly how we start with customers at Webs / Siloy Benelux. Before anyone builds anything, we run a short working session — an AI Opportunity Scan — that maps your commercial process and produces a ranked list of where AI would genuinely return, and which of those frictions are a fit for a custom agent versus a plain workflow. No credits, no build — just a clear picture of what's worth doing. And if you already know the one friction you want gone, we build that agent with you and prove it on your own data before you scale it.
So: are you planning to build your first custom agent in HubSpot — and do you already know which process you'd point it at?
If you want to think that through — what needs an agent, and what just needs a decent workflow — send me a message.