From Support Ticket to Resolution: Build a Sequential AI Agent Pipeline

Agentspan is presented as a durable orchestration layer for creating, integrating, and observing AI-agent workflows, with support for agents built through its SDK or imported from frameworks such as LangGraph, OpenAI Agents SDK, and Google ADK. The post demonstrates its sequential pipeline strategy, expressed with the `>>` operator, through a support-ticket workflow in which specialized agents classify an incoming ticket, draft a customer response, and determine whether engineering escalation is needed. Using an urgent performance issue from an enterprise customer as an example, the pipeline produces structured priority and issue tags, a tailored public reply, and an actionable internal engineering note. Agentspan runs these steps as server-side workflows that persist execution state, allowing a run to resume after client or process failures rather than repeating completed work, while its dashboard provides visibility into inputs, outputs, tool calls, and token use. The workflow can be connected to Zendesk through credential-managed tools to retrieve tickets, post public responses, and create private escalation notes, and it can be extended linearly with additional agents such as Jira ticket creators or translators; the next series installment covers running agents in parallel instead.

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