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Standard AI agents suffer from drift. They often forget the original goal once they get three steps deep into a task.

Most people think an AI agent is a tool that executes a prompt. In reality, a standard agent is like a calculator: it performs one operation perfectly but doesn't know why it is doing it.

A long-horizon agent acts like a project manager. It maintains state management, meaning it keeps track of what has been completed and what remains across an entire engagement.

This shift changes how we handle substantive testing in audit. Instead of an analyst manually gathering invoices and matching them to bank statements, the agent manages the end-to-end flow.

The mechanism relies on a global plan and traces. A trace is a chronological log of every action and decision the agent makes.

This turns a day of manual evidence gathering into a review process. The analyst stops ticking boxes and starts auditing the agent's decision tree.

I condensed the transition from task-bots to long-horizon agents into a 12-page visual field guide.

How do you currently handle state management when an AI workflow spans multiple days or different data sources?

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