How to get your Zendesk Sunshine data ready for agentic AI

Zendesk Sunshine holds the custom customer data that ties your support operation to the rest of the business — subscription status, device history, order records, loyalty tiers, and other account details your team defines to fit its own model rather than a generic ticketing schema. Getting your Zendesk Sunshine data ready for agentic AI means centralizing your custom objects and relationships in a warehouse or data lake, where AI agents can query the full customer picture, join it with support history and other systems, and answer questions on demand. Fivetran + dbt Labs delivers the complete data foundation agents need for this: Fivetran moves Zendesk Sunshine data reliably into your warehouse or data lake, and dbt transforms it into trusted, AI-ready tables.

Why Zendesk Sunshine data is critical for agentic AI

Support teams build custom objects in Zendesk Sunshine because tickets alone don't tell the whole story. A customer's subscription tier, device history, renewal date, or product configuration all shape how an issue should get handled and who should own it. That context typically stays locked inside Zendesk, disconnected from the systems that actually drive support decisions. When an agent needs to know whether a customer holds a premium plan or has an open warranty claim, someone pulls that context by hand, ticket by ticket. As support volume grows, custom object records accumulate faster than any team can review manually, and by the time a pattern in device failures or renewal churn reaches a support ops leader, the window to act on it has often closed. Zendesk Sunshine data needs infrastructure built for agents, not just analytics, so it moves at the speed support actually operates.

What agentic AI can do with Zendesk Sunshine data

Once Zendesk Sunshine data is centralized and modeled, AI agents turn it into immediate, usable answers.

A support ops manager can ask an agent to flag every customer with an active subscription or device record who has also opened 3 or more tickets in the past 30 days, without writing a query or building a report first.

A support leader can have an agent match custom account attributes — warranty status, account tier, contract type — against ticket volume to identify which customer segments drive the highest support cost, and get the answer in seconds.

A support rep can ask an agent for the full custom-object history behind an account before a call, pulling subscription changes, device swaps, and order records into a plain-language summary instead of clicking through multiple linked records.

A VP of Support can ask an agent to surface every account approaching renewal that also shows a support pattern, such as repeated escalations or declining satisfaction scores, so retention outreach happens automatically instead of after a customer has already decided to leave.

How Fivetran gets your Zendesk Sunshine data ready for agentic AI

Zendesk Sunshine data isn't built for agent-scale use in its raw form. Custom objects and the custom relationships that connect them — to accounts, tickets, and each other — live as fragmented records inside Zendesk, changing constantly as customers switch plans, swap devices, or place new orders. Fivetran moves this data out of Zendesk Sunshine and into your warehouse or data lake, capturing every custom object and relationship, including deletions, so your account picture stays complete rather than a snapshot from whenever someone last exported it. Fivetran keeps the data fresh with reliable, ongoing syncs, so agents query current information instead of stale exports. Once the data lands, dbt Labs transforms it into tables that are centralized, cleansed, and governed — modeling the relationships between custom objects, testing for accuracy, and documenting them for reliable agent use. Together, Fivetran + dbt Labs deliver the complete movement-to-transformation stack this data needs. For teams standardizing on an open architecture, the Fivetran Managed Data Lake Service gives Zendesk Sunshine data a home that works across analytics and AI workloads alike.

What your Zendesk Sunshine data unlocks for your team

With Zendesk Sunshine data in a central warehouse, AI agents unlock capabilities your support team couldn't access before.

• Unified customer context — agents pull subscription status, device history, and order records together with ticket history for a single, complete view of any account.
• Faster account triage — agents flag high-value or high-risk accounts based on custom attributes the moment a ticket arrives, not after manual review.
• Pattern detection across custom data — agents spot trends in device failures, renewal timing, or plan changes that would take a person days to notice.
• Automated relationship tracking — agents follow how custom objects connect to accounts and tickets, keeping outreach and escalations tied to the right context.
• An open, interoperable foundation — support data sits alongside sales, product, and finance data, so agents can answer questions that span the full customer relationship.

FAQ

What does it mean for Zendesk Sunshine data to be AI agent-ready?

Zendesk Sunshine data is AI agent-ready when your custom objects and relationships are centralized in a warehouse or data lake, kept current, and modeled into clean tables an agent can query directly. That means an agent pulls account context — subscriptions, devices, orders — without a person exporting or reformatting it first.

What can my team actually do with AI agents and Zendesk Sunshine data?

Support teams can have agents summarize account history before a call, flag high-risk accounts based on custom attributes, spot patterns across device or subscription records, and connect support activity to the rest of the customer relationship, all without building a report first.

Is Zendesk Sunshine data ready for AI agents out of the box?

Not without preparation. Zendesk Sunshine data needs to be centralized, modeled, and governed before an agent can query it reliably.

Do we need a data engineering team to set this up?

No. Fivetran moves Zendesk Sunshine data into your warehouse or data lake without custom scripts, and dbt handles the transformation layer, so a support ops team can get started without a dedicated data engineering team behind it.

How does Fivetran get Zendesk Sunshine data ready for AI agents?

Fivetran moves Zendesk Sunshine data — including custom objects, custom relationships, and deletions — reliably into your warehouse or data lake, keeping it fresh and complete. dbt Labs then transforms and governs that data into clean, documented, and AI-ready tables. Together, Fivetran + dbt Labs deliver the full movement-to-transformation stack support teams need for agentic AI.

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