Monitor, evaluate, and operate multi-agent AI solutions in Azure

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Implement distributed observability infrastructure for production multi-agent AI solutions using OpenTelemetry and Azure Monitor. Design distributed tracing architectures with correlation propagation, implement structured logging for agent decision paths, configure telemetry aggregation, and build anomaly detection for agent behavior patterns.
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
Design evaluation frameworks for production multi-agent AI solutions using Microsoft Foundry. Define success metrics for coordination quality and system-level outcomes, implement calibrated LLM-as-judge patterns, design synthetic test datasets for agent collaboration scenarios, and build regression pipelines for behavioral drift detection.
By the end of this module, you're able to:
- Define multi-agent success metrics that capture coordination quality, handoff effectiveness, and system-level outcomes
- Implement calibrated LLM-as-judge patterns designed for evaluating complex multi-agent chain quality
- Design synthetic test datasets that comprehensively exercise multi-agent collaboration scenarios and edge cases
- Build regression testing pipelines that detect behavioral drift across agent and model updates
Optimize multi-agent performance and cost in Microsoft Foundry. Design model routing strategies across agent ecosystems, implement multi-level caching architectures, optimize token usage and context management across agent chains, and systematically analyze quality-cost-latency trade-offs.
By the end of this module, you're able to:
- Design model routing strategies that assign optimal model tiers to agents based on task complexity
- Implement multi-level caching architectures that reduce redundant computation across agent interactions
- Optimize token usage and context management to reduce cost across multi-agent chains without sacrificing quality
- Analyze and balance quality-cost-latency trade-offs systematically at the multi-agent system level
Design human-in-the-loop systems for production multi-agent workflows using Power Automate and Microsoft Teams. Implement confidence-threshold escalation architectures, design asynchronous approval workflows for high-stakes agent actions, build human feedback collection and active learning pipelines, and configure audit workflows that provide complete human oversight records for regulated environments.
By the end of this module, you're able to:
- Design confidence-threshold escalation architectures that route uncertain agent decisions to appropriate reviewers
- Implement asynchronous approval workflows for high-stakes agent actions using Power Automate and Microsoft Teams
- Build human feedback collection and active learning pipelines that continuously improve agent quality
- Configure audit workflows that provide complete human oversight records for regulated environments
Debug and respond to production incidents in multi-agent AI solutions in Azure. Implement agent replay capabilities that reproduce complex multi-agent failures, apply structured root cause analysis procedures for multi-agent incident diagnosis, configure automated detection and remediation for common agent failure patterns, and establish incident response and post-mortem processes adapted to the characteristics of AI agent system failures.
By the end of this module, you're able to:
- Implement agent replay capabilities that reproduce complex multi-agent failures in production environments
- Apply structured root cause analysis procedures for diagnosing multi-agent system failures
- Configure automated detection and remediation for common agent failure patterns
- Establish incident response and post-mortem processes adapted to the characteristics of AI agent system failures

Syllabus

Implement distributed observability for multi-agent solutions with OpenTelemetry
- Introduction
- Design distributed tracing for multi-agent solutions
- Implement structured logging for agent decisions
- Configure telemetry aggregation and dashboards
- Build anomaly detection for agent behavior
- Module assessment
- Summary
Design evaluation frameworks for multi-agent solutions with Microsoft Foundry
- Introduction
- Define multi-agent success metrics
- Implement LLM-as-judge evaluation for multi-agent systems
- Design synthetic test datasets for multi-agent evaluation
- Build regression testing pipelines to detect agent drift
- Module assessment
- Summary
Optimize multi-agent performance and cost in Microsoft Foundry
- Introduction
- Design model routing for agent ecosystems
- Implement multi-level caching strategies
- Optimize token usage and context management
- Balance quality, cost, and latency tradeoffs
- Module assessment
- Summary
Design human-in-the-loop approval workflows with Power Automate and Microsoft Teams
- Introduction
- Design confidence-based escalation for human intervention
- Implement approval workflows for agent-initiated actions
- Build active learning from human feedback
- Configure audit workflows for regulated decisions
- Module assessment
- Summary
Debug and respond to production multi-agent incidents in Azure
- Introduction
- Implement agent replay for production debugging
- Design root cause analysis for agent failures
- Configure automated incident detection and remediation
- Establish incident response and post-mortem processes
- Module assessment
- Summary