JITA Learning · Microsoft · Field Guide
AI-500 · Designing and Implementing Multi-Agent AI Solutions (beta)
Build working judgment for multi-agent AI solution architecture.
What you should expect
Build working judgment for multi-agent AI solution architecture.
This Field Guide develops a working mental model of multi-agent AI solution architecture. You will connect decompose goals into agents, workflows, tools, and ownership boundaries, choose orchestration, communication, memory, and handoff patterns across agents, design MCP servers, clients, APIs, functions, and tool interfaces, and apply RAG, caching, token, guardrail, and human-review strategies across agent workflows. You are not just memorizing feature names; you are learning what owns the requirement, what can fail, and how to verify the outcome.
What you will learn
- decompose goals into agents, workflows, tools, and ownership boundaries
- choose orchestration, communication, memory, and handoff patterns across agents
- design MCP servers, clients, APIs, functions, and tool interfaces
- apply RAG, caching, token, guardrail, and human-review strategies across agent workflows
- recognize why one technically plausible alternative loses under the stated requirement
- apply Changed Fact reasoning and verify the design or implementation through operational evidence
Decision skills you will practice
- identify the governing requirement
- compare the nearest plausible alternative
- apply Changed Fact reasoning
- trace prerequisites, dependencies, and failure paths
- verify configured state, effective state, runtime outcome, and negative-path evidence
Who this is for
AI architects and engineers designing coordinated multi-agent systems with tools, memory, grounding, guardrails, and production controls
Use this when you need to learn the architecture, mechanics, dependencies, tradeoffs, failure paths, and verification logic behind the certification.
Decisions you will learn to distinguish
- single-agent expansion vs true multi-agent decomposition
- tool-call failure vs orchestration or agent-reasoning failure
- local agent success vs end-to-end workflow correctness
Why JITA is different
Microsoft explains the platform. JITA teaches the decision.
Microsoft Learn explains the services, features, and documented behavior that support multi-agent AI solution architecture. JITA connects those capabilities into decisions: what owns the requirement, what the nearest alternative is, what changes when one fact changes, and what evidence proves the implementation worked.
How to choose:
Use the Field Guide to build understanding. Use the Exam Preparation Guide to test whether that understanding survives exam-style decisions.