Focus on this objective today: Set up AI solutions in Foundry: Configure model and agent deployments. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Implement distributed observability for multi-agent solutions with OpenTelemetry Module 7 Units Feedback Advanced AI Engineer DevOps Engineer Solution Architect Azure Microsoft Foundry Azure Monitor Implement distributed observability infrastructure for production multi-agent AI solutions using OpenTelemetry and Azure Monitor. Design distributed tracing architectures that propagate correlation context across agent service boundaries, implement structured logging frameworks that capture agent decision paths, configure telemetry aggregation pipelines, and build anomaly detection for abnormal agent behavior patterns. Learning objectives By the end of this module, you're able to: Design distributed tracing architectures that propagate correlation context across multi-agent boundaries Implement structured logging frameworks that capture agent decision paths, reasoning traces, and tool invocations Configure telemetry aggregation pipelines for multi-agent observability at production scale Build anomaly detection that identifies abnormal agent behavior patterns and triggers actionable alerts Add Prerequisites Before starting this module, you should have: Experience with OpenTelemetry instrumentation and Azure Monitor for AI applications Familiarity with Log Analytics workspaces and Kusto Query Language (KQL) Experience from the Analyze and debug your generative AI app with tracing module or equivalent Experience building multi-agent systems with Microsoft Foundry Agent Service Proficiency in Python Get started with Azure Choose the Azure account that's right for you.
Objective
Set up AI solutions in Foundry: Configure model and agent deployments