Turning support tickets into organization-wide intelligence

Every company has a goldmine of valuable information trapped in its support tickets. These daily interactions contain customer feedback, bug reports, and pain points — yet most teams never tap into them. The data is often siloed, making it difficult for teams like Research & Development (R&D) or Customer Success (CS) to extract actionable insights without manually digging through endless ticket histories.

To solve this, we developed an internal "Support AI Hub" built on Port's Agentic Engineering Platform (AEP). This hub isn't just another support tool — it's a collection of specialized agents designed to surface insights for your entire organization. Together, these agents deliver:

• Faster ticket intelligence and insights
• Curated context sharing for ticket escalations
• Proactive customer health checks
• Monthly ticket trend analysis

Below, we'll walk through how we leveraged Port's Agentic Engineering Platform (AEP) to create a hub of agents that serve multiple teams across several common support scenarios. The agents achieve this by tapping into Port's Context Lake, which contains aggregated and correlated data from our support stack, including Mixpanel, HubSpot, Zendesk, Jira, and Slack.

1. Faster ticket intelligence

Our first challenge was making it faster and easier to understand a support ticket. The obvious starting point was a Summarize Ticket agent that could distill a long support interaction into a concise, insightful paragraph — giving a support engineer the context they need to jump straight into solving the issue.

That simple summary proved so useful for individual ticket context that we extended the concept to support handovers between engineers providing follow-the-sun coverage.

Rather than a brief paragraph, the Handover Support Ticket agent generates a comprehensive brief that can be copied directly into an internal note. It includes a full chronological timeline (e.g., "Anna did this, then we opened a Jira bug"), steps to reproduce, additional observations, current hypotheses capturing where the previous engineer's investigation left off, instructions on how to verify the fix, and immediate next steps for the new owner. The result is a detailed handover with the full context of the situation.

2. Escalation "translator" to bridge the Support–R&D gap

Effective collaboration often comes down to clear communication. When support escalates an issue to an R&D team — whether for a quick consultation or to create a Jira ticket — the engineer has to recall or track down the specific information and format that the R&D team expects. That formatting friction can slow the entire process down.

The Ticket Summary + Escalation Assistant agent acts as a "translator" to solve this. It automatically generates escalation drafts using pre-defined templates tailored to each R&D team's preferences. These templates ensure that crucial details are included from the start, such as:

• Customer environment
• Integration version
• Deployment type

This agent has significantly improved collaboration by ensuring R&D receives the precise information they need, in the format they prefer — eliminating back-and-forth and accelerating problem resolution.

3. Proactive customer health checks

While summarizing individual tickets is useful, true strategic value comes from understanding a customer's experience over time. Account teams would love to know, "What pain points has Customer X experienced in the last two weeks?" Having a clear picture of recent issues is the kind of priceless context you want right before a customer meeting.

The Cluster Zendesk Tickets agent provides exactly that context, along with actionable insights. Here is a recent scenario that highlights the impact:

• Customer X complained on a call to their account team about "GitLab instabilities."
• The account team escalated the most recent ticket, and the R&D team responded asking for more specific details.
• Running the agent was a lightbulb moment. It returned a precise list of the GitLab issues the customer had reported — delivering the specifics R&D needed to turn a vague, high-level complaint into a focused, data-driven conversation and enabling our team to address the root problems effectively.

4. Trend analysis (and learning as we go)

The ultimate goal is to move from reactive analysis and fixes to proactive trend identification.

Agents like the Recurring Tickets Analyzer are designed to sift through multiple tickets and uncover systemic issues. The strategic goal is to provide support managers, R&D, executive teams, and product teams with a clear view of what's burning in production — helping them decide where to focus their investment.

This agent is still a work in progress. The quality of the trend analysis isn't where we want it yet, given the large volume of data and context required to perform the analysis well. That said, it will continue to improve through the iterative process of building, testing, and refining.

5. Measuring agent usage and impact

To understand the effectiveness and impact of these agents, it's important to monitor them and gather usage metrics. Configuring these agents to collect metrics — and having a Port dashboard to display usage and performance — helps answer key questions and provides visibility into:

• Total Invocations: How often are the agents being used?
• Usage Over Time: Which agents are most popular, and when?
• Performance: Key operational data like agent response times.
• Run Status: Tracking completed, failed, and in-progress runs to immediately spot issues. This is also crucial for troubleshooting, allowing us to investigate exactly why a run failed.
• Team Usage: Visualizing which teams (R&D, Product, Support) are leveraging the hub, proving its org-wide value.

These metrics are essential for measuring the value and adoption of the agents in the Support AI Hub and ensuring its continued health and relevance across the organization.

What insight will you unlock next?

Building a Support AI Hub didn't just increase efficiency — it helped rewire how our entire organization gains insights through better customer intelligence. By making these insights accessible, teams across the business are empowered to make faster, more informed decisions grounded in real customer experiences.

If you could quickly build AI agents that unlock data and provide insights for your organization, what problem would you solve?

Think building an agent is too hard? It's not! Check out Build an AI Agent in Port to see how easy it is for agents to access the Context Lake of integrated data from your platform.