The agent works—but every run costs more, behaves differently, and is difficult to debug.

In this session, we will refactor an over-agented expense-approval workflow. The original agent reads receipts, interprets policy, selects an approver, updates records, and handles exceptions.

We will replace each responsibility with the simplest reliable mechanism:

- Structured extraction for receipt data
- Deterministic rules for policy checks
- A workflow engine for routing and approvals
- Human review for high-risk exceptions
- An agent only where bounded judgment adds value

The comparison will show how each change affects reliability, latency, cost, security, and observability.

Attendees will leave with a practical method for reducing unnecessary autonomy without removing useful AI. The goal is not to eliminate agents. It is to make every remaining agentic decision justify its complexity.