Do not treat this objective as a recognition exercise. Practise explaining and applying 'Optimize cost and performance: Recommend strategies for license and resource optimization' in a new scenario, then check your reasoning against the official source. Source context: Read in English Optimize multi-agent performance and cost in Microsoft Foundry Module 7 Units Feedback Advanced AI Engineer Solution Architect Azure Microsoft Foundry 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. Learning objectives 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 Add Prerequisites Before starting this module, you should have: Experience building multi-agent systems with Microsoft Foundry Agent Service Familiarity with Azure Cache for Redis caching patterns Understanding of token usage and cost monitoring in Microsoft Foundry Experience with OpenTelemetry instrumentation for performance measurement Proficiency in Python Get started with Azure Choose the Azure account that's right for you.
Objective
Optimize cost and performance: Recommend strategies for license and resource optimization