AI applications promise transformative capabilities but introduce unique failure modes, such as hallucinations, latency spikes, data drift, and cost overruns, that traditional SRE practices don’t fully address. This session explores proven reliability patterns, retries with exponential backoff, circuit breakers, graceful degradation, and human-in-the-loop guardrails tailored for Microsoft Foundry workloads, where observability covers agent traces, quality metrics, and safety evaluations. You’ll see live demos of implementing these in Foundry projects using zone-redundant storage, multi-region failover, and built-in content filters to achieve production-grade resilience while maintaining trust and compliance.

Benefits of attending the webinar – Attendees will learn how to move from proof of concept to production by applying concrete reliability patterns that address the real failure modes of generative AI systems in Azure AI Foundry. They will also leave with practical guidance on HA/DR architecture, model failover, observability, and responsible AI controls that improve trust, compliance, and service continuity

Is there a demo?

• Demo 1: Implementing reliability protections in an Azure AI Foundry app, including retry logic, fallback handling, and graceful degradation.
• Demo 2: Hardening the solution with zone-redundant dependencies such as Azure Storage, Azure Cosmos DB, and Azure AI Search, plus secondary-region failover design.
• Demo 3: Implementing API Management implementation for the API GW scenario.