An artificial intelligence (AI) agent can process an invoice, help adjudicate a claim, or classify a support ticket in a proof of concept. But running these agents across thousands or even millions of work items in a production environment introduces an entirely different set of challenges. At enterprise scale, success depends on much more than the agent itself. Organizations need to track the state of each work item as it moves through multiple agents and systems, surface exactly where failures occur and why, allow a human to step in when needed, and scale the infrastructure dynamically based on demand. Amazon Quick Automate addresses these operational challenges through case management. Every work item is represented as a case that persists throughout its lifecycle, providing step-by-step visibility into workflow state, supporting human-in-the-loop processing where judgment is needed, and enabling parallel execution so teams can orchestrate agents to automate business processes at scale.

In this post, we show you how to combine case management with agentic automation capabilities in Quick Automate. We introduce case management and explore the lifecycle of cases in an agentic workflow from case creation through processing to resolution. We cover how to create and manage single or multiple cases, automatically track and update status, handle exceptions, and incorporate Human-in-the-loop (HITL) steps within workflows. We also show the case creator-processor pattern that enables dynamic scaling. Finally, we walk through how to structure case management for enterprise processes, including HITL and case tracking, through a real-life use case.