Apply task decomposition and agent collaboration strategies in Microsoft Foundry
Advanced
AI Engineer
Solution Architect
Azure
Microsoft Foundry
Foundry Agent Service
Apply advanced task decomposition strategies in Microsoft Foundry multi-agent systems. Design prompt chaining workflows for complex reasoning, implement LLM-driven adaptive decomposition with meta-agent planners, configure agent handoff protocols for context-preserving transitions, and balance decomposition granularity against coordination overhead.
Learning objectives
By the end of this module, you're able to:
- Design prompt chaining workflows that guide agents through complex multi-step analytical tasks
- Implement dynamic adaptive task decomposition using LLM-driven meta-agent planning
- Configure agent collaboration and handoff protocols for context-preserving task transitions
- Balance task decomposition granularity against coordination overhead for performance optimization
Prerequisites
Before starting this module, you should have:
- Experience building multi-agent solutions with Microsoft Foundry Agent Service and Microsoft Agent Framework
- Understanding of orchestration patterns from the previous module or equivalent experience
- Proficiency in Python
- 4+ years of AI/ML development experience
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