
HubSpot's launch of Breeze AI has definitely turned some heads, promising to change how marketing, sales, and service teams operate. But after the initial "wow" moment, the practical questions start bubbling up. How do you actually control this thing? What’s the real cost going to be? And how do you stop it from going completely off the rails?
If you don't have a clear governance plan, you're essentially flying blind. You could end up with an inconsistent brand voice, unpredictable bills thanks to a new credits system, and AI agents acting on incomplete or just plain wrong information. It's the difference between having a helpful co-pilot and a chaotic robot you've left to its own devices.
This guide will walk you through the essentials of Breeze AI feature governance. We’ll break down what it can do, shed some light on its limitations, and give you a framework for rolling out AI in a way that’s both safe and genuinely useful.
Understanding HubSpot Breeze AI
Before we get into the governance side of things, let's make sure we're all on the same page about what HubSpot Breeze is. It's not one single product but more of an umbrella brand for all the AI tools baked into HubSpot’s customer platform.
So, what are we actually talking about? It breaks down into a few key parts:
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Breeze Copilot: This is your AI sidekick that lives inside your HubSpot workflow. It’s built to help with daily tasks like summarizing customer records, drafting emails, or brainstorming content ideas.
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Breeze Agents: Think of these as autonomous workers. Each agent is designed to handle a specific, complete task for different teams. For instance, the Content Agent can write a blog post, the Prospecting Agent can find and contact leads, and the Customer Agent can field support questions.
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Breeze Intelligence: This is the data engine humming along in the background. It adds company details to your CRM records and uses buyer intent signals to help you spot which prospects are showing the most interest.
The core pillars of Breeze AI feature governance
To get a real handle on Breeze, you need a plan that covers three key areas: controlling your data, putting up guardrails for automation, and keeping a close eye on costs.
Managing access and data sources
HubSpot’s main pitch is its unified data model. Breeze is designed to pull context from your entire CRM, from structured data like contact info to unstructured stuff like emails and call notes. The goal is to give the AI the full picture so it can be as helpful as possible.
But this "all-in-one" approach has a catch. It can be tough to selectively block an AI agent from seeing certain information. For example, your customer support agent probably doesn't need access to sensitive internal sales forecasts. Since all the data is in one big pool, it’s tricky to stop the AI from using irrelevant or confidential context. You might find an agent oversharing information or pulling from the wrong document.
A much simpler and more secure way to handle this is with a platform built for control. With eesel AI, you can take a "Scoped Knowledge" approach. Instead of plugging your AI into one giant, all-or-nothing data source, you point each agent only to the specific information it needs. A support agent can be limited to your public help center, a specific Confluence space, or a folder of approved playbooks in Google Docs. This method keeps your agents on-topic and using only approved information from the get-go.
This infographic illustrates how eesel AI's "Scoped Knowledge" approach centralizes information from various sources to enhance Breeze AI feature governance.
Setting up workflow and automation guardrails
Breeze Agents are built to run tasks by themselves. You can use the "Breeze Studio" and Marketplace to set up triggers that tell the agents when to jump into action, like having a Customer Agent automatically reply to new support emails.
The problem? Unleashing a fully autonomous agent without proper testing is a huge gamble. How do you know it will use the right tone with a frustrated customer? Or that it will correctly follow your complex process for escalating urgent issues? Often, the customization is limited to preset triggers that may not line up with how your business actually works.
This is where a dedicated testing environment is absolutely essential. Tools like eesel AI include a Simulation Mode that lets you safely test your AI agent on thousands of your actual past support tickets. You can see exactly how it would have replied to real customer issues, get accurate predictions on resolution rates, and fine-tune its behavior in a safe sandbox before it ever talks to a single customer.
A screenshot of eesel AI's Simulation Mode, a key tool for Breeze AI feature governance that allows for safe testing and refinement of AI agent behavior.
A flexible workflow engine is equally important. Instead of being stuck with rigid automation rules, a platform like eesel AI gives you full control. You can define precisely which tickets the AI should handle and what Custom Actions it can perform, whether it's simple tagging and triaging or making live API calls to look up order info in Shopify or update a record in your internal database.
Understanding the credit system and performance monitoring
HubSpot is moving to "HubSpot Credits" to manage Breeze AI usage. It's a consumption-based model where different AI actions, like enriching a contact or having a conversation, chew through your monthly credit allowance.
The headache with credit-based systems is budget uncertainty. A busy month with high support volume could lead to a surprisingly large bill or, worse, a service interruption if you run out of credits. This model can end up penalizing you for growing your business and makes financial forecasting a real challenge.
Predictable pricing makes a world of difference. In contrast, eesel AI offers transparent and predictable pricing based on a set number of monthly AI interactions, with no per-resolution fees. Your bill is the same every month, so you can budget properly without worrying about usage spikes. This transparency lets you scale your AI with confidence, not fear. Best of all, you can start with a monthly plan and cancel anytime, while many enterprise tools lock you into an annual contract from day one.
A screenshot of the eesel AI pricing page, showing a transparent, predictable model that is a core component of effective Breeze AI feature governance.
Feature | HubSpot Breeze AI | eesel AI |
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Pricing Model | Consumption-based "Credits" | Flat-rate "AI Interactions" |
Cost Predictability | Low (Varies with usage) | High (Fixed monthly cost) |
Per-Resolution Fees | No, but conversations consume credits | No |
Budgeting | Difficult to forecast | Simple and straightforward |
Onboarding | Requires sales calls / demos | Fully self-serve, go live in minutes |
HubSpot Breeze pricing and plans
HubSpot's pricing can be a bit of a maze, but figuring out how Breeze features and credits are assigned is a must for any governance plan. The features aren't just "on" or "off", they're tied to specific, and often pricey, subscription tiers.
Breeze feature availability by Hub
To get the full set of Breeze Agents, you’ll need to be on the highest tiers of several HubSpot products.
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Content Agent: Comes with Content Hub Pro+ plans.
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Social Media Agent: Comes with Marketing Hub Pro+ plans.
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Customer Agent: Comes with Service Hub Pro+ plans.
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Prospecting Agent: Comes with Sales Hub (Pro+ and higher).
The HubSpot Credits model explained
The new credits model is a big change in how you'll pay for HubSpot.
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How it Works: Your HubSpot account gets a monthly allowance of credits based on your plan, usually somewhere between 500 and 5,000. Every time an AI feature does something, it uses up some of those credits.
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Consumption Rates: Based on early info, a single data enrichment action costs about 10 credits, while a single AI conversation with a customer can cost 100 credits.
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The Governance Challenge: This model turns you into a credit watchdog. You have to constantly monitor usage to avoid going over or having your service cut off. It also makes calculating the ROI of your AI much harder, since the real cost is hidden behind an abstract credit system. A busy support month could cost you a lot more than you planned.
eesel AI’s transparent pricing as an alternative
If that sounds complicated, that's because it is. There’s a much simpler way.
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The eesel AI Model: Instead of a confusing credit system, eesel AI uses a straightforward, tiered model based on the number of "AI interactions" you use per month. One interaction is simple: it’s one AI reply or one AI action (like tagging a ticket). That's it.
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Benefits: This gives you complete cost predictability. You pick a plan that matches your expected volume, and your bill is never a surprise. It also offers the flexibility of month-to-month billing, which is great for teams that want to start small and scale without getting locked into a long-term contract.
Here's what that looks like in practice:
Plan | Monthly (bill monthly) | Effective /mo Annual | Bots | AI Interactions/mo | Key Unlocks |
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Team | $299 | $239 | Up to 3 | Up to 1,000 | Train on website/docs; Copilot for help desk; Slack; reports. |
Business | $799 | $639 | Unlimited | Up to 3,000 | Everything in Team + train on past tickets; MS Teams; AI Actions (triage/API calls); bulk simulation. |
Custom | Contact Sales | Custom | Unlimited | Unlimited | Advanced actions; multi-agent orchestration; custom integrations; custom data retention; advanced security. |
A 90-day implementation plan for Breeze AI feature governance
Rolling out AI responsibly doesn’t have to be a massive, year-long undertaking. A structured 90-day plan is a great way to get moving. You can use this framework whether you're using Breeze or another AI platform.
Phase 1 (Days 1-30): Foundation and piloting
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Define Scope: Don't try to boil the ocean. Pick one or two low-risk, high-impact tasks to start with. Think simple, repetitive questions like "where is my order?" or "how do I reset my password?"
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Connect Knowledge: Limit your AI to a very specific set of knowledge base articles or macros that are directly related to your pilot tasks. This stops it from guessing or giving answers on topics you haven't approved.
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Establish Guardrails: In the AI's core instructions, be explicit about its tone of voice. More importantly, define the exact situations where it must hand off a ticket to a human. For example, "If a customer mentions 'refund' or 'angry,' immediately escalate to the support team."
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Pro TipIf your platform has it, run a simulation before you do anything else. With eesel AI, you can test this entire pilot setup on thousands of your past tickets to check its performance and get a real-world preview of your resolution rate.
Phase 2 (Days 31-60): Activation and monitoring
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Go Live: Once you're happy with the pilot, turn on the AI agent, but do it slowly. Start with a single support channel (like email) or only enable it for a specific group of customers.
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Monitor Performance: Keep a close eye on the numbers: automated resolution rate, customer satisfaction (CSAT) on AI-handled tickets, and how often it escalates to a human. For Breeze, this is also when you need to start watching your credit consumption closely.
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Identify Gaps: Use your reports to see where the AI is getting stuck or escalating tickets most often. This is an incredibly useful exercise because it almost always points to gaps in your knowledge base. With eesel AI, you can even automatically generate draft knowledge base articles from successful human replies, helping you fill those gaps with proven answers.
This image shows an eesel AI dashboard for monitoring performance and identifying knowledge gaps, a critical step in a Breeze AI feature governance plan.
Phase 3 (Days 61-90): Scaling and optimization
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Expand Scope: Once the pilot is running smoothly, you can start to scale. Gradually give the AI more complex ticket types to handle or turn it on for other channels like your website chat.
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Refine and Enhance: Use the performance data you've collected to make the AI smarter. Tweak its instructions, adjust its workflows, and add more advanced skills. For example, you could add a custom action that lets the AI look up an order status directly from your e-commerce platform, allowing it to solve the ticket without any human help.
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Formalize Governance: Now that you have a process that works, write it down. Create clear internal rules on who can edit AI prompts, who's responsible for reviewing performance reports, and how new automation workflows get approved.
Take control of your AI strategy with Breeze AI feature governance
HubSpot's Breeze AI is a big step toward a more integrated, AI-driven customer platform. But to really make the most of it, you need a thoughtful approach to Breeze AI feature governance. Your success will come down to how well you can manage data access, set clear automation guardrails, and keep a tight rein on your costs.
This brings up a fundamental trade-off. While all-in-one platforms like HubSpot offer convenience, that ease sometimes comes at the expense of the detailed control, transparent pricing, and robust testing features you need to deploy AI with total confidence.
For teams that value flexibility, predictability, and a fast, self-serve setup, a dedicated AI layer is often the smarter move. eesel AI plugs right into your existing helpdesk like Zendesk or Freshdesk and all your knowledge sources. It gives you the power to launch highly-controlled, effective, and predictable AI agents in minutes, not months.
Frequently asked questions
Effective Breeze AI feature governance prevents common pitfalls like inconsistent brand voice, unpredictable costs due to credit consumption, and AI agents acting on incomplete or incorrect information. It ensures your AI tools are a helpful co-pilot, not a chaotic force, by providing clear control and direction.
Under Breeze AI feature governance, managing data access is critical. While HubSpot's unified data model can make selective blocking tricky, focusing on limiting AI agents to only the specific, approved information sources they need for their tasks is key. This prevents them from accessing or oversharing irrelevant or confidential data.
For strong Breeze AI feature governance, it's essential to set up a dedicated testing environment, like a simulation mode, to validate agent behavior on real past data before deployment. Define explicit instructions for tone, escalation protocols, and the exact situations where an agent must hand off to a human, ensuring controlled and predictable operation.
Managing costs under the HubSpot Credits model for Breeze AI feature governance requires diligent monitoring of consumption rates. To maintain budget predictability, it's crucial to continuously track credit usage against your monthly allowance to avoid unexpected bills or service interruptions from usage spikes.
A structured 90-day plan is effective for Breeze AI feature governance, starting with defining a low-risk pilot scope, connecting specific knowledge, and establishing guardrails. Subsequent phases involve gradually activating the AI, rigorously monitoring its performance to identify gaps, and then scaling while refining its intelligence and formalizing internal rules.
Yes, for more granular control over Breeze AI feature governance, dedicated AI layers like eesel AI offer a "Scoped Knowledge" approach for data access and a simulation mode for rigorous testing. These platforms provide transparent, predictable pricing models based on AI interactions, offering flexibility beyond HubSpot's credit system and feature tiers.