HubSpot Data Agent: AI-Powered Data Intelligence for Your CRM

40% of CRM data becomes outdated (Enricher.io).

Sales reps waste time on outdated info. Marketing targets wrong people. CS teams miss churn signals.

What this guide covers:

• How Data Agent works in Data Hub

• Three ways it creates intelligence

• Business impact on data quality

• Who gains from this?

• Setup and effective practices

What is HubSpot Data Agent?

Data Agent is an AI-powered intelligence layer for your CRM. It transforms fragmented, unstructured data into clean, unified intelligence. You'll find it in the Breeze Marketplace under HubSpot AI Agents.

It works on any HubSpot objects—contacts, companies, deals, tickets, or custom objects.

Three Ways Data Agent Creates Intelligence

Data Agent operates through three components:

Smart Properties: AI enriches your CRM fields using external sources. Data Agent fills these properties on a schedule using research from the web, documents, and other sources, instead of manually updating fields like funding rounds or tech stacks.

Smart Columns combines your CRM data with new sources—like Google Sheets, integrated apps, or external datasets—to create enriched datasets in Data Studio. You can analyze information inside and outside your CRM together.

Smart Actions adds intelligence to your workflows. It uses AI to personalize, segment, and trigger automation based on synthesized insights rather than basic field values.

Enrich records with Smart Properties, blend data sources with Smart Columns, or enhance workflows with Smart Actions.

How It Fits in Data Hub

Data Agent adds AI intelligence to your Smart CRM. Your CRM holds records and history. It transforms that data into clear, unified intelligence.

Data Hub is HubSpot's data management layer, which has six components:

• Data Agent (smart properties, columns, actions)

• Data Integration (syncing apps)

• Event Management (custom events)

• Data Quality (duplicates, formatting, enrichment)

• Data Model (custom objects)

• Data Studio (advanced reports)

Data enrichment lives under Data Quality, not as a separate tool. Data Agent is how HubSpot performs AI-powered enrichment.

The relationship: Data Hub brings outside data into HubSpot. Data Quality keeps that data clean and enriched. Data Agent does the AI-powered work of transforming fragmented information into unified intelligence across all five data sources.

[Diagram: Data Hub architecture showing Data Agent within the Data Quality layer]

How HubSpot Data Agent Works

Data Agent pulls info from five sources to create insights.

The five sources:

• Your CRM: Contact, company, and deal information

• Calls: Recorded conversation history

• Emails: Email threads and communication

• Documents: Your playbooks and stored content

• The web provides current information about companies and trends.

Intelligence Creation Process

When you create a Smart Property or Smart Action with a question:

• Step 1: Data Agent reads your question.

• Step 2: Searches all five sources simultaneously.

• Step 3: Combines findings and cites sources.

• Step 4: Writes results to Smart Properties, Smart Columns, Smart Actions, or notes.

• Step 5: Uses HubSpot Breeze credits based on query complexity.

Auto Intelligence with Smart Properties

Smart Properties make Data Agent operate continuously.

Old way: New record arrives → Rep searches for data (15–30 min) → Rep updates CRM → Total: 15–30 minutes.

With Data Agent, the process takes a total of 2–3 minutes.

The steps are: a new record arrives → Smart Property creates intelligence → Data fills in → Workflow routes based on data.

Data Agent saves time and shifts capacity from manual updates to more strategic work.

Benefits for GTM Teams

Data Agent helps in three ways: unified data, saved time, and more efficient operations.

Unified Data Quality

The problem is that fragmented data causes routing errors, missed opportunities, and wasted outreach.

The fix is that Data Agent unifies information into clean intelligence.

81% of HubSpot users report improved data quality with the platform's data management tools (HubSpot 2025).

When your CRM has unified intelligence, your operations run on facts, not assumptions.

What enhances:

• Routing works correctly.

• Segments remain accurate.

• Forecasts use actual data.

• Teams trust the CRM.

More Time for Teams

Sales, marketing, and CS teams spend hours on data tasks that Data Agent manages.

Sales wins:

• Faster account insights

  • Funding news

  • Tech stacks

• Better call prep

  • Recent company actions

  • Decision maker info

• More selling

Marketing wins:

• Better targeting

  • Right company sizes

  • Accurate Industries

• Signal-based lists (funding, hiring)

• Better personalization

CS wins:

• Spot churn early.

  • Engagement declines.

  • Competitor threats

• Find upsell opportunities.

  • Growth signals

  • New budgets

• Risk flags before renewals

Example time savings by team:

Based on common usage patterns for teams using automated intelligence workflows

Smarter RevOps

Data Agent enables intelligence-driven operations.

Trusted forecasts: Unified data makes pipeline and revenue forecasts dependable, not guesses.

Better tracking: Marketing demonstrates ROI by connecting outside signals (funding, growth, leadership changes) to conversions.

Continuous quality: Maintain quality through ongoing intelligence instead of cleanup projects.

Signal-based work: Route leads, trigger sequences, and alert reps based on unified intelligence, not isolated data points.

Data Agent creates lasting value at the transition from cleanup projects to continuous intelligence.

74% of organizations achieve ROI within the first year (Google Cloud 2025).

Who Should Use Data Agent

Data Agent works best for teams with fragmented data and significant intelligence needs.

Best Fits

High-speed sales teams need automated intelligence. Teams reaching 50+ new records weekly find manual data updates a constraint.

Account-based marketing: ABM needs account intel for a personal touch. Data Agent creates context without hiring researchers.

CS teams with 100+ accounts can't manually monitor every customer. Automated intelligence surfaces growth and churn signals early.

If you're responsible for CRM data quality, Data Agent shifts you from fixing bad data to creating unified intelligence.

Common Uses

Account intelligence: Recent funding, technology stacks, hiring trends, competitor information, decision makers

Deal enrichment includes company news, recent projects, organizational changes, strategic focus, and pain points.

Customer insights: Growth signals, churn risks, competitor threats, industry trends, renewal context

Ticket intelligence: Customer sentiment, issue patterns, product usage context, escalation triggers

When to Use It

If you want to choose Data Agent, then:

• Manual data tasks take more than 5 hours weekly per team.

• Fragmented data hinders routing or forecasts.

• You run ABM or targeted campaigns.

• Teams need unified intelligence for decisions.

• You're reactive, not anticipatory.

Wait if:

• Data volume is low (<10 records/week).

• Your data quality is outstanding.

• The budget is limited, so first fix the basic HubSpot.

• The team hasn't learned essential CRM workflows.

The ROI test: if Data Agent delivers value by justifying the credit cost, if getting back 10+ hours weekly and unified intelligence.

Setting Up Data Agent

Setup has three phases: prepare, configure, optimize.

What You Need First

Right HubSpot plan: Data Agent works with Marketing Hub or Sales Hub (Starter, Professional, or Enterprise). Check your plan and Breeze credits.

Clean your CRM first. A clean CRM needs accurate domains, no duplicates, and standard field values. Data Agent needs this. Poor data in means poor intelligence out.

Clear process: Before writing prompts, know the needed intelligence. Map relevant fields for routing and scoring.

Setup Steps

1. Pick high-value questions: Start with 3–5 that generate insights (tech stack, recent funding, employee growth, competitors, industry challenges).

2. Create Smart Properties, Actions, or Columns: Choose how Data Agent creates intelligence. Make prompts specific and measurable. Set update frequency. Test with samples first.

3. Set automation: Choose when Data Agent should create intelligence (new record creation for qualified records, status changes, stage changes, scheduled updates).

4. Connect workflows: Link intelligence to actions (route hot leads, alert reps, trigger sequences, flag CS accounts)

[Screenshot: Smart Property setup showing Data Agent prompt configuration]

Best Practices

Save credits by creating intelligence only for high-value records (score >70). Schedule batch updates. Use specific prompts. Monitor credit usage.

Write better prompts: Be specific. For example, "Find Series B+ funding in the last 12 months" instead of "find funding." Include timeframes. Test before scaling.

Check quality by spot-checking intelligence weekly. Compare AI-generated data to manual findings. Adjust prompts based on accuracy.

Govern it: Define Smart Properties creators. Document standard prompts. Review credit usage regularly.

Conclusion

Your path to unified intelligence with Data Agent:

Start with accurate data:

• Remove duplicates and adjust field formats.

• Pick fields relevant for routing and scoring.

• Write valuable intelligence questions.

Set up intelligent automation:

• Create 3. Smart Properties with specific prompts.

• Connect intelligence to workflows and actions.

• Test samples before scaling.

Monitor and enhance:

• Track credit usage.

• Measure time savings.

• Adjust prompts based on results.

Data Agent works best in a process-first RevOps approach. First, map your data model, set routing, and build workflows, then add AI intelligence. Create a system where unified data drives action.

Teams achieving the best results treat data as a strategic asset, with continuous intelligence replacing traditional cleanup projects.