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JITA Learning · Certification

AI-500 · Designing and Implementing Multi-Agent AI Solutions (beta)

Learn what each publication is designed to do, how the two guides work together, and choose the guide that matches your next step.

AI-500 study guide and exam preparation resources

Preparing for Designing and Implementing Multi-Agent AI Solutions (beta) (AI-500)? JITA Learning provides a complementary Field Guide and Exam Preparation Guide designed to build practical understanding first, then test decision-making through certification-focused scenarios and changed-fact reasoning.

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Microsoft · Artificial Intelligence

AI-500 · Designing and Implementing Multi-Agent AI Solutions (beta)

Professional·Field Guide + Exam Preparation Guide
Choose your learning path

Each publication has a different job. Start with the guide that matches what you need now, or use them in sequence for the most complete JITA learning experience.

Field Guide

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.

You’ll 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
Exam Preparation Guide

Prove your AI-500 judgment across multi-agent AI solution architecture under exam-style conditions.

This Exam Preparation Guide tests whether your understanding of multi-agent AI solution architecture survives realistic decision pressure. You will practice separating single-agent expansion vs true multi-agent decomposition, tool-call failure vs orchestration or agent-reasoning failure, and local agent success vs end-to-end workflow correctness. Each scenario is designed to make you identify the governing fact, reject the nearest plausible alternative, and explain the evidence that keeps the correct answer correct.

You’ll 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
How the guides complement each other

Understand it first. Then prove you can apply it.

The Field Guide builds the understanding. Use it when you need the architecture, mechanics, dependencies, tradeoffs, failure paths, and verification logic behind the certification.

The Exam Preparation Guide tests the judgment. Use it when you need to practice under exam-style conditions, distinguish close answers, and understand why the correct answer survives changed facts.

Recommended sequence: Field Guide → Exam Preparation Guide → return to the Field Guide for targeted remediation where needed.