AI-103 Complete Guide: Developing AI Apps and Agents on Azure

Microsoft Certified: Developing AI Apps and Agents on Azure (AI-103) is a new Associate-level certification for engineers building generative AI applications and AI agents on Azure, going GA on June 30, 2026. It is the de facto successor to AI-102 (Azure AI Engineer Associate), with the focus pivoting decisively from "composing classic Azure AI Services" to "generative-AI and AI-agent-centric implementation". That makes it the most notable Associate certification of 2026, the year Microsoft is investing most heavily in AI.

This article walks through the AI-103 exam spec (based on pre-GA information), the differences from AI-102, the exam scope centered on Azure AI Foundry, implementation tips for RAG / Agent / Responsible AI, and a 3-4 month roadmap to passing. Voucher campaigns are very likely to be plentiful right after GA, so for AI engineers who want a first-mover advantage in this emerging field, the second half of 2026 is the best timing.

AI-103 Exam Basics (Pre-GA Information)

AI-103 follows the standard Associate-tier spec; at GA the expected parameters are 120 minutes, 40-60 questions, a passing score of 700 / 1000, 165 USD / 21,103 JPY, and 12 months validity (renewable via the renewal assessment). You can sit it via Pearson VUE, either through OnVUE online or at a testing center. Japanese localization will likely arrive a few months after GA rather than at launch, but should eventually match the multilingual support AI-102 offers. Beyond multiple-choice items, expect Python / C# code-reading questions, scenario questions on prompt engineering and RAG pipeline design, and case studies.

From AI-102 to AI-103: What Changed

AI-103 is not a routine annual refresh of AI-102; it is a restructure that pivots the coverage area itself toward generative AI. The weight of the classic Azure AI Services (Computer Vision, Speech, Language Understanding, QnA Maker, Bot Service) that AI-102 stressed has clearly shrunk, replaced at the core by Azure OpenAI, Azure AI Foundry, Azure AI Agent Service, RAG with Azure AI Search, Semantic Kernel, Prompt Flow, and Responsible AI evaluation. In Microsoft's certification strategy, you can see the intent to rebrand the Azure AI Engineer for the generative-AI and agent era.

Because of this shift, even AI-102 holders need fresh study for AI-103. Specifically, you need to catch up across the board on areas AI-102 did not dive into: Azure AI Foundry project structure, Agent Service tool-calling patterns, Prompt Flow DAG design, and RAG accuracy metrics such as Faithfulness, Groundedness, and Relevance.

Domain 1: Plan and prepare AI solutions on Azure (15-20%, estimated)

This domain covers launching and designing an AI project. The core topics are creating an Azure AI Foundry project and its Hub/Project hierarchy, model selection (OpenAI gpt-4o / gpt-4o-mini / o1 / o3 / Phi series / open models), Responsible AI evaluation design (Content Safety, fairness, privacy), security design (Private Endpoint, Managed Identity, Customer-Managed Key), and cost estimation (token-based pricing model).

Domain 2: Implement generative AI solutions (30-35%, most important)

This is the highest-weighted core domain and the single biggest determinant of pass or fail. The core topics are Azure OpenAI Service model deployment (Standard / Provisioned / Global), prompt engineering (designing System / User / Assistant messages, few-shot prompting, Chain of Thought), RAG patterns (Azure AI Search vector/hybrid retrieval + OpenAI generation), Prompt Flow LLM workflows (DAG form), and model evaluation (built-in metrics: Groundedness, Relevance, Coherence, Fluency, Faithfulness, and custom LLM-as-a-Judge).

Implementation is tested in both Python and C#. Especially common are choosing between the Azure OpenAI SDK and the OpenAI Python SDK in Python, handling streaming responses, tracking token usage, and defining schemas for Function Calling / Tool Calling.

Domain 3: Develop AI agents (20-25%, new area)

This is the headline new domain added in AI-103. The core topics are building managed agents with Azure AI Agent Service (formerly the Azure AI Assistant API), multi-agent orchestration with Semantic Kernel (Microsoft's agent SDK), integrating Tools / Functions (Code Interpreter, File Search, Bing Grounding, custom functions), conversation state management (Thread / Run / Message lifecycle), and multi-agent patterns (Sequential, Concurrent, Group Chat, Handoff).

The Agent area evolved rapidly during 2024-2026, so Microsoft's official resources, Microsoft Build 2026 sessions, and the Azure AI Foundry sample repositories are the freshest study material. Third-party books will be virtually nonexistent immediately after GA, so reading Microsoft Learn and GitHub samples directly is a mandatory part of the study style.

Domain 4: Implement traditional AI capabilities (15-20%, inherited from AI-102)

This domain inherits the classic Azure AI Services area from AI-102; its weighting shrinks but does not disappear. The core topics are Document Intelligence (formerly Form Recognizer) for form OCR, Azure AI Speech (Text-to-Speech / Speech-to-Text / Speech Translation), Azure AI Vision (Image Analysis 4.0, Face API, Optical Character Recognition), and Azure AI Language (sentiment analysis, summarization, key-phrase extraction, PII detection, custom NER / classification). For AI-102 holders this domain is largely review, but newcomers need to go through the API patterns and SDK usage end-to-end.

Domain 5: Monitor and maintain AI solutions (10-15%, estimated)

This domain covers the production-operations phase. The core topics are model performance monitoring (token consumption, latency, error rate, model quality score), content safety operations (Prompt Shield, Groundedness Detection, Protected Material detection via Azure AI Content Safety), cost optimization (using Provisioned Throughput Units, caching, model-size selection), and model update management (canary deployments to new versions, prompt versioning). Generative-AI-specific topics like hallucination detection, prompt-injection defense, and data leakage prevention are new areas that did not exist in AI-102.

A 3-4 Month Pass Roadmap

This is a 3-month plan assuming you already hold AI-901 and have Python experience.Month 1: Work through the Microsoft Learn AI-103 learning path (generative AI track), create a project in Azure AI Foundry, deploy OpenAI models, and practice basic prompt engineering.Month 2: Build one RAG pattern (Azure AI Search + OpenAI), construct an evaluation pipeline in Prompt Flow, and create one Agent with tool calls in Agent Service.Month 3: Review the classic AI Services, run Responsible AI evaluations, implement Content Safety, and drill the official Practice Assessment until you score 80%+. Newcomers to Python or AI should add 2-3 months upfront for Python fundamentals and AI-901 study, making a 5-6 month total plan realistic.

What to Aim for After AI-103

If you want to level up as an AI developer, Microsoft currently has no Expert-tier AI certification, so AZ-305 (Solutions Architect Expert) is the realistic next step to acquire architecture skills that include AI. For data crossover, reinforce the ML side with DP-100 (Data Scientist Associate) or the data-platform side with DP-700 (Fabric Data Engineer Associate). For people targeting enterprise AI rollouts, the combo of AI-103 + AZ-305 + SC-300 (Identity Admin) lets you establish the 'someone who can safely roll out AI across the org' position. Outside Microsoft, pairing AI-103 with multi-platform certifications like OpenAI, Anthropic, or Google Cloud's Generative AI Leader is becoming increasingly valued on the AI engineer job market.

Frequently Asked Questions

Exam information in this article is based on the Microsoft Learn official certifications page and the Azure AI Foundry official documentation. AI-103 is scheduled to GA on June 30, 2026, so the exam scope, weightings, and fee in this article include estimates based on pre-GA information. This article is not an official Microsoft Corporation product, and has no affiliation or sponsorship relationship. Microsoft, Azure, Microsoft Entra, and Azure OpenAI are trademarks of the Microsoft group of companies. Information is based on public official materials as of May 24, 2026. Always check the official pages for the latest information.