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Support is a chain of work. Someone reads the ticket, finds the customer’s context, checks the actual system, asks a colleague for a decision, makes a change, verifies it, and tells the customer what happened. Every handoff consumes time and risks losing evidence or ownership. Studio lets you build AI agents around that entire responsibility. Connect intake, knowledge, operational tools, human decisions, and customer response into a workflow that can continue across stages and waits. Define what a resolved case looks like, then make the agent work toward that result.

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Carry the case through to resolution

The unit of work is a support case with an outcome. An agent can read the request through a configured help-desk connector, investigate through authorized tools, request approval for a proposed change, execute the approved request, and verify the result before preparing the customer response. A durable run preserves progress when the agent needs an answer, a later check, or additional budget. Your team can see why it is waiting and respond from the Work area. The workflow can also dispatch work to another permitted agent when the case needs a separate specialty. Read Run the full support loop for the complete operating model, including escalation and closure.

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Connect communication to the actual work

A support workflow needs both sides of the job: doing the work and keeping people informed. Native communication routes and cloud tools have defined availability. The support-loop guide explains Slack, Teams, email, and infrastructure access so you can design a workflow that works with your deployment.

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Put your operating knowledge to work

Org Brain supports the agents with organizational evidence: shared facts, work-product records, and learned knowledge. An agent can search organization-visible evidence when its channel permits the tool. Human members can also inspect authorized channel evidence and ask for cited answers. Use knowledge to choose the next investigation or understand an earlier fix. Use live systems to establish what is happening now. Keep customer-specific data in the appropriate scoped system; a fact shared organization-wide is available beyond one customer channel. After a run, review its outcome and keep useful lessons at the right visibility. Worker improvement proposals require a person to apply them, so a successful pattern can become a reviewed workflow revision.

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Keep your people at the decision points

Give the agent the responsibility and tools for a bounded support workflow. Give people the decisions that require their authority or judgment.
  • Route missing information, exceptions, and approvals to the right responders.
  • Review the exact request before a connector changes a system or sends a message.
  • Keep service credentials and permissions scoped to the customer and operation.
  • Inspect run timelines, transcripts, artifacts, and costs when a result needs explanation.
  • Use the desktop’s terminals, browser, remote access, and Copilot for hands-on work when the case needs it.
The cloud workflow and the operator workspace serve the same support outcome. Their execution and credential boundaries remain separate.

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Measure resolved work

Start with one queue and compare like-for-like cases. Use Outcomes, run history, and your support system to assess:
  • Whether the requested outcome was verified.
  • How often the agent needed a human decision or a manual handoff.
  • Time spent working and waiting.
  • Cost per completed case, including retries and follow-up work.
  • Whether customer updates were accurate and the case was closed or escalated correctly.
Define these success criteria before scaling. A completed run alone does not prove that the customer problem was solved; the workflow needs an explicit verification step.

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Build your first support agent

  1. Choose a recurring support request and define its resolution and escalation criteria.
  2. Connect its ticketing system and the smallest set of required operational tools.
  3. Configure an AI support agent with investigation, approval, action, verification, and response stages.
  4. Test successful, failed, and human-assisted cases from intake through the final update.
  5. Enable the required triggers, assign responders, and review outcomes before increasing scope.

Run the full support loop

Follow a request from the support queue to a verified resolution or useful escalation.

AI support agents

Set up stages, connections, human tasks, triggers, and budgets.