How to Add Evaluation, Cost Controls, and Observability to a Multi-Agent AI System

A technical guide outlines how to harden a multi-agent customer support system built on Microsoft Azure AI Foundry for real production use. The approach centers on continuous evaluation using a G-Eval-style method, where a separate model scores live production outputs daily against criteria such as correctness, tone, and escalation appropriateness. A drop in escalation scores is flagged as the highest-priority alert, as it signals the system is making risky decisions without human oversight. On the cost side, the guide recommends comparing Provisioned Throughput against pay-as-you-go pricing quarterly, since traffic growth often shifts the breakeven point sooner than teams anticipate. Consistent Azure resource tagging across all agents is also advised to enable direct cost attribution without manual reconciliation.