How to Build an AI Agent for HubSpot and Jira

The workflow problem

Customer support teams and engineering teams operate in different universes. Support lives in HubSpot, managing tickets, conversations, and customer relationships. Engineering lives in Jira, managing sprints, epics, and technical debt. When a customer reports a bug through HubSpot, bridging that gap is a manual, error-prone process that frustrates everyone.

A support agent receives a bug report via HubSpot ticket. They troubleshoot, confirm it is a genuine bug, and now need to escalate to engineering. They open Jira, try to find if a similar issue exists (the search rarely helps because support and engineering describe the same problem in different language), create a new ticket, and attempt to include enough technical detail for engineering to act on it. They often lack the technical vocabulary to describe the issue precisely, so the Jira ticket says something vague like “Button doesn’t work for customer” without reproduction steps, browser version, or error logs.

Engineering triages the Jira ticket, cannot reproduce the issue, asks for more information through a Jira comment that the support agent never sees (because why would support check Jira?), and the ticket stalls. Meanwhile, the customer follows up in HubSpot: “Any update on my bug?” The support agent checks Jira, sees the comment requesting more info, goes back to the customer, gets the details, updates Jira, and the cycle repeats.

When the bug is finally fixed, nobody tells the customer. The Jira ticket moves to “Done” but HubSpot is not updated. The customer either assumes nothing happened or opens a duplicate ticket.

Why an AI agent, not just automation

A basic integration can create a Jira ticket when a HubSpot ticket is tagged “bug.” But the resulting Jira ticket is a bare-bones entry with the HubSpot ticket title and maybe a link back. It contains no technical context, no reproduction steps, no severity assessment, and no customer impact analysis.

An AI agent reads the full HubSpot conversation thread – every message between the support agent and the customer – and extracts the technical details needed for a useful Jira ticket. It identifies the steps to reproduce, the expected versus actual behavior, the customer’s environment (browser, OS, API version), and any error messages or screenshots shared. It writes the Jira ticket in engineering language, not customer language.

The agent also performs intelligent deduplication. Before creating a new Jira ticket, it searches existing issues using semantic similarity, not just keyword matching. A customer reporting “the export button gives a blank file” gets matched to an existing Jira issue about “CSV export returns empty response body for datasets >10K rows,” even though the wording is completely different.

Critically, the agent maintains the bidirectional link. When the Jira ticket’s status changes, the agent updates the HubSpot ticket. When engineering adds a comment requesting information, the agent translates it into a customer-friendly follow-up and posts it to the HubSpot conversation. The customer and the engineer never need to leave their own platform.

How it works with ACP

ACP authenticates with HubSpot via OAuth 2.0 and with Jira via Atlassian OAuth 2.0 (using Jira Cloud’s OAuth app model). The agent operates as a continuous bridge between customer support and engineering workflows.

HubSpot tools available: Read tickets, conversations, and contact records. Update ticket status and properties. Add notes and timeline events to tickets. Read custom properties (like product area, severity, and customer tier). Create and update tickets. Search the knowledge base for existing solutions.

Jira tools available: Create issues with full field population (summary, description, priority, labels, components, epic link, story points). Search issues with JQL. Read issue status, comments, and changelog. Add comments to issues. Transition issue status. Read sprint and board data. Attach files to issues.

The agent processes the workflow in both directions. When a HubSpot ticket is confirmed as a bug, the agent reads the conversation, extracts technical details, searches Jira for duplicates, and either creates a new issue or links to an existing one. It then monitors both platforms for updates.

Here is a realistic end-to-end scenario:

HubSpot ticket #4892, submitted by customer: “When I try to export my dashboard data to CSV, I get a file that’s completely empty. This has been happening since yesterday. I’m on Chrome 122, the dashboard has about 15,000 data points.”

Support agent replies in HubSpot: “Thanks for reporting this. I’ve confirmed the issue on our end. Can you try with a smaller dataset to see if it’s size-related?”

Customer responds: “Yes! If I filter to under 10,000 rows it works fine. Over that it’s empty.”

Agent creates Jira ticket BUG-1847: Summary: CSV export returns empty file for datasets exceeding 10,000 rows Priority: High (affects Enterprise customer, data export is a core feature) Description: Customers report that CSV export from dashboards produces an empty file when the dataset exceeds approximately 10,000 rows. Exports under 10K rows function correctly. Steps to reproduce: 1. Navigate to any dashboard with >10K data points. 2. Click Export > CSV. 3. Open downloaded file – it is empty (0 bytes content). Environment: Confirmed on Chrome 122. Regression, began approximately [date]. Customer impact: Enterprise customer (Acme Corp, ARR $48K), data export is a daily workflow. Related: Possible duplicate of BUG-1790 (CSV timeout on large datasets), but that issue was marked resolved in Sprint 42.

Agent updates HubSpot ticket #4892: “This has been escalated to our engineering team as a high-priority issue (internal reference: BUG-1847). We’ve confirmed it’s related to export size limits. I’ll update you as soon as we have a fix timeline.”

[Two days later, Jira BUG-1847 moves to “Done”]

Agent updates HubSpot ticket #4892: “Great news – our engineering team has fixed the CSV export issue for large datasets. The fix will be deployed in tonight’s release. You should be able to export dashboards of any size starting tomorrow morning. Please let us know if you run into any further issues.”

Governance and security

Customer data flowing from HubSpot to Jira introduces compliance considerations, especially for companies handling PII or financial data.

HubSpot OAuth scopes: The agent requests tickets for ticket read/write, crm.objects.contacts.read for customer context (name, company, tier), and conversations.read for accessing support conversations. It does not request access to deals, invoices, or marketing data. PII exposure is limited to what is necessary for engineering context.

Jira OAuth scopes: The agent requests read:jira-work and write:jira-work for issue management. It does not request admin scopes, user management, or project configuration access. The agent can only create issues in projects it has been granted access to.

PII handling: The agent can be configured to redact PII from Jira tickets. Customer names can be replaced with anonymized identifiers; email addresses and phone numbers can be stripped. The Jira ticket links back to the HubSpot ticket for support agents who need the full customer context, but engineering sees only the technical details.

Audit trail: Every ticket creation, status update, and cross-platform comment is logged with timestamps, user identities, and the specific data transferred. Support managers can audit which customer issues were escalated, how quickly engineering responded, and whether customers were notified of resolutions.

Example use cases

• Automated escalation: When a HubSpot support ticket is tagged “bug-confirmed,” the agent creates a fully detailed Jira issue with customer impact context, reproduction steps, and environment details extracted from the conversation.

• Duplicate detection: Before creating a new Jira ticket, the agent searches for semantically similar existing issues and alerts the support agent if a match is found, preventing engineering from investigating the same bug twice.

• Customer notification on fix: When a Jira issue transitions to “Done” or “Released,” the agent posts a customer-friendly update to the linked HubSpot ticket, closing the communication loop without manual intervention.

• Priority escalation: If a bug affects a customer tagged as “Enterprise” or “Strategic” in HubSpot, the agent sets the Jira priority to “Critical” and adds the customer’s ARR and tier to the ticket, ensuring engineering prioritizes accordingly.

• SLA reporting: The agent tracks the time between HubSpot bug report and Jira resolution, generating weekly reports showing average fix time by customer tier, product area, and severity, helping both support and engineering leadership identify bottlenecks.

Getting started

Connect your customer support and engineering workflows today:

1. Sign up at cloud.agenticcontrolplane.com and create a workspace.
2. Connect your tools by authenticating HubSpot and Jira (Atlassian Cloud) through the Data Sources page. Select which Jira projects should receive escalated tickets.
3. Describe your agent in plain English: “When a HubSpot support ticket is confirmed as a bug, create a detailed Jira ticket with reproduction steps, and notify the customer in HubSpot when the Jira issue is resolved.” The agent will confirm its workflow before activating.