Course Program Overview
AI-103 Azure AI Application & Agent Developer
- Duration: 3 Months
- Format: Live Online / Classroom / Blended / Corporate
- Sessions: 5 per week
- Session Length: 01 Hour of each session
- Tech Stack: Azure OpenAI Service, Semantic Kernel, AutoGen, Prompt Flow, Azure AI Search, LangChain, Vector Databases, Microsoft Copilot Studio, Azure App Service.
- Outcome: Industry-grade portfolio + Microsoft Certification + Career acceleration
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Our placement Record
đź“Ś Key Exam Information
Certification Name
Microsoft Certified: Azure AI Application & Agent Developer Associate (2026 Edition)
Exam Code
AI-103
Level
Intermediate / Associate (Requires foundational knowledge of AI-901 or basic programming)
Audience
Software engineers, AI developers, and cloud architects specializing in building autonomous agents and GenAI applications.
Format
Case studies, multiple-choice, and lab-based performance tasks.
Number of Questions
Approximately 45–60
Duration
100 minutes (plus 20 minutes for setup)
Passing Score
700 out of 1000
Delivery
Online proctored or at authorized test centers
Languages Available
English, Japanese, Korean, Simplified Chinese, German, French
🎯 Who Should Take AI-103
- Software Engineers & Full-Stack Developers Developers looking to integrate advanced Generative AI capabilities and autonomous agents into modern web and mobile applications using the Microsoft Azure ecosystem.
- AI Architects & Cloud Consultants Architects responsible for designing scalable, secure, and production-ready AI infrastructures that utilize Large Language Models (LLMs) and multi-agent orchestration.
- Data Engineers & MLOps Professionals Specialists who want to master the transition from simple model deployment to the creation of complex, stateful AI agents that can interact with enterprise data and external APIs.
🧠Exam Domains & Weightage
20–25%
Design and implement AI Agent infrastructures
Master the architecture of autonomous agents, including task planning, memory management, and tool integration using frameworks like Semantic Kernel and AutoGen.
25–30%
Develop Generative AI solutions with Azure OpenAI
Focus on deploying Large Language Models, optimizing tokens, and implementing advanced prompt engineering techniques to ensure high-quality application responses.
20–25%
Implement Retrieval-Augmented Generation (RAG) & Vector Search
Learn to connect AI models to real-time enterprise data using Azure AI Search and Vector Databases, ensuring agents provide accurate and context-aware information.
20–25%
Orchestrate multi-agent workflows and security
Design systems where multiple AI agents collaborate to solve complex problems, while implementing strict content safety, monitoring, and enterprise security protocols.
🏆 Benefits of AI-103 Certification
- Leader in the Agentic AI Market: Hold the primary 2026 certification for AI Agents, positioning you as a top-tier candidate for the fastest-growing job sector in technology.
- Expertise in Microsoft’s AI Stack: Demonstrate mastery over specialized tools like Semantic Kernel and Prompt Flow, which are essential for enterprise-grade AI development.
- High-Impact Salary Potential: Associate-level certifications in Agentic AI are currently commanding the highest premiums in the market, often leading to 45 LPA+ opportunities.
- Verified Production Readiness: Proves you can not only "prompt" an AI but also build, secure, and scale a complete autonomous system in a professional cloud environment.
🌟 Top 5 Reasons to Choose Adian Solutions for this course
Leading-Edge Agentic Curriculum
While others teach basic chatbot integration, Adian Solutions focuses on the 2026 shift toward Autonomous AI Agents. Our curriculum is built around the AI-103 blueprint, covering multi-agent orchestration, task planning, and memory management using industry-leading frameworks like Semantic Kernel and AutoGen.
Certification Mastery & Advanced Retake Support
We are committed to your success at the Associate level. If you do not pass the AI-103 exam on your first attempt, we provide specialized "Deep-Dive" sessions and extended lab access at no extra cost. Our team manages your exam readiness through rigorous mock tests and scenario-based simulations.
Production-Grade Agentic Labs
Experience true engineering by building stateful AI agents in our advanced lab environment. You will gain hands-on expertise in configuring Vector Databases, implementing complex RAG (Retrieval-Augmented Generation) pipelines, and deploying agents that can interact with real-world APIs and enterprise data.
Expert Portfolio & Agentic Capstone
Develop a high-impact professional portfolio through our Capstone Project. You will architect a "Multi-Agent Ecosystem" capable of collaborative problem-solving. This project demonstrates your ability to handle complex, production-level AI challenges, making you an immediate asset to global employers.
Elite Placement in the AI Sector
Leverage Adian’s exclusive network of 1,000+ global recruiters. We provide targeted career support for high-end roles, including AI Architect and Agent Developer positions. Our placement program ensures your technical certification translates into a high-paying 45 LPA+ career outcome.
Skills You Will Get
Through this course, you will evolve into a sophisticated AI Application Developer, mastering the tools required to build the next generation of autonomous systems. You will gain expert-level proficiency in orchestrating multi-agent workflows, optimizing Large Language Models (LLMs), and securing AI applications for enterprise use. By completing our high-intensity technical labs and a complex agentic capstone project, you will build a robust portfolio that proves your ability to solve intricate business problems. Finally, you will command the 2026 job market with verified skills in Semantic Kernel, Vector Search, and Agentic MLOps, opening doors to elite global opportunities.
Autonomous Agent Orchestration
Autonomous Agent Orchestration
Multi-Agent Frameworks (AutoGen & Semantic Kernel)
Multi-Agent Frameworks (AutoGen & Semantic Kernel)
Advanced RAG & Vector Databases
Advanced RAG & Vector Databases
Generative AI App Development
Generative AI App Development
Technical Prompt Engineering & Flow
Technical Prompt Engineering & Flow
Agentic Security & Content Safety
Agentic Security & Content Safety
API & Tool Integration
API & Tool Integration
Production-Ready Agentic Capstone
Production-Ready Agentic Capstone
Course Program
(3 Months)
Classroom/Live online 03 months
(80+ Hours)
This three-month AI-103 program is an intensive technical journey designed to transform developers into elite AI Agent Architects. The curriculum begins with the structural design of autonomous agents, mastering task planning, and memory management using the Semantic Kernel framework. In the second month, the focus shifts to advanced Generative AI implementation, where students deep-dive into Azure OpenAI orchestration, prompt engineering, and the integration of high-performance RAG (Retrieval-Augmented Generation) pipelines using Vector Databases. The final month is dedicated to the cutting-edge field of Multi-Agent Systems, teaching students how to build collaborative ecosystems where multiple agents solve complex enterprise problems. The program culminates in a sophisticated Capstone Project—architecting an end-to-end autonomous multi-agent system—ensuring students are 100% prepared for the AI-103 certification and the highest-paying roles in the 2026 global market.
â—Ź Month 1
Exam Coverage: 20–25%
Agent Infrastructure & Task Orchestration
In the first month, learners master the architectural blueprints of autonomous systems. We move beyond simple chatbots to build "Reasoning Agents" that can plan complex tasks, manage stateful conversations, and utilize long-term memory. Students get hands-on experience with the Semantic Kernel framework, learning how to define plugins, planners, and memories. A significant focus is placed on "Tool-Use," where agents are taught to interact with external systems and APIs to execute real-world actions. By the end of this month, participants will be able to design a single-agent infrastructure capable of autonomous decision-making.
Practical Labs:
Learners will configure a stateful agent using Semantic Kernel, implement custom plugins for external API calls, and build a persistent memory layer using Azure AI Search.
Case Studies:
Analyzing how a global logistics firm uses autonomous agents to dynamically reroute shipments based on real-time weather and traffic data APIs.
Exam Weightage Covered
This month covers Design and Implement AI Agent Infrastructures (20–25%), establishing the core logic required for autonomous system development.
â—Ź Month 2
Exam Coverage: 45–55%
Generative AI Mastery & RAG Architecture
The second month focuses on the "Brain" and "Knowledge" of the agent. Learners explore advanced Azure OpenAI Service configurations, focusing on model selection, token optimization, and latency management. We deep-dive into the technical implementation of Retrieval-Augmented Generation (RAG), teaching students how to convert enterprise data into vectors and store them in Vector Databases. Students master "Prompt Flow" to create repeatable, testable AI pipelines. This month emphasizes the transition from generic model responses to context-aware, data-driven intelligence that respects enterprise privacy guardrails.
Practical Labs:
Students will build a production-grade RAG pipeline using Azure AI Search and Vector Search, optimize LLM performance using Prompt Flow, and implement PII (Personally Identifiable Information) filtering for data safety.
Case Studies:
Exploring how a healthcare provider utilizes RAG-powered agents to summarize patient records securely while adhering to strict HIPAA-level privacy standards.
Exam Weightage Covered
This month covers Developing Generative AI Solutions (25–30%) and Implementing RAG & Vector Search (20–25%), focusing on the intelligence layer of the certification.
â—Ź Month 3
Exam Coverage: 20–25% + Consolidation of 100%
Multi-Agent Systems & Enterprise Capstone
The final month introduces the most advanced tier of 2026 AI: Multi-Agent Orchestration. Using frameworks like AutoGen, students learn to design systems where specialized agents (e.g., a Researcher Agent, a Coder Agent, and a Reviewer Agent) collaborate to complete high-level objectives. We cover advanced security protocols, including defense against prompt injection and adversarial attacks. The program culminates in the Enterprise Capstone Project, where students architect a full multi-agent ecosystem. This project integrates all skills—from vector storage to collaborative reasoning—ensuring complete mastery of the AI-103 objectives.
Practical Labs:
Learners will build a multi-agent "Software Development Team" using AutoGen, implement jailbreak protection guardrails, and deploy the entire system to Azure App Service for production testing.
Case Studies:
Examining how a FinTech giant uses a multi-agent ecosystem to perform real-time fraud detection, legal compliance checking, and customer notification simultaneously.
Capstone Project
Students will architect an "Autonomous Enterprise Consultant"—a multi-agent system that can ingest market data, generate financial forecasts, and produce a formatted PDF report with zero human intervention.
Certification Weightage Covered
This month covers Orchestrating Multi-Agent Workflows and Security (20–25%), completing the 100% technical blueprint for the Associate exam.
Real Roles. Real Results.
Explore Your Post-Course Career
After completing the course, learners can unlock high-impact roles such as Machine Learning Engineer, Cloud Solutions Architect, Data Scientist, and AI Product Lead—across sectors like technology, BFSI, healthcare, retail, and manufacturing.
Machine Learning Engineer
Designs, trains, and optimizes ML models for prediction, classification, and automation across industries like finance, healthcare, and manufacturing.
Data Scientist
Extracts insights from structured and unstructured data using statistical analysis, visualization, and modeling techniques. Drives decision-making and business intelligence.
AI Solutions Architect
Leads the design and deployment of scalable AI systems using cloud platforms, MLOps pipelines, and enterprise-grade frameworks
Deep Learning Researcher
Specializes in neural networks, CNNs, RNNs, GANs, and transformers. Builds models for image recognition, NLP, and generative tasks.
MLOps Engineer
Implements CI/CD pipelines, containerization, and cloud-native deployment for AI models. Ensures reliability, scalability, and performance monitoring.
Generative AI Developer
Builds intelligent applications using LangChain, Hugging Face, and LLMs. Applies RAG pipelines and transformer models to domains like legal tech and marketing.
Computer Vision Engineer
Develops image and video analysis systems using OpenCV, TensorFlow, and deep learning. Works on facial recognition, object detection, and OCR.
NLP Engineer
Creates language models and conversational AI systems using NLTK, SpaCy, and transformers. Powers chatbots, sentiment analysis, and document summarization.
AI Product Lead
Bridges technical teams and business strategy. Oversees AI product lifecycle—from ideation to deployment—ensuring alignment with market needs and ethical standards.
Frequently Asked Questions
AI-103 Azure AI Application & Agent Developer
AI-103 is the 2026 Associate-level certification from Microsoft that replaces the older AI-102. It focuses specifically on building "Agentic AI"—autonomous systems that can plan, reason, and use tools to complete complex tasks.
AI-901 is a Fundamentals course for understanding AI concepts. AI-103 is a technical, engineering-heavy course designed for professionals who actually want to build and deploy production-grade AI agents and applications.
A basic understanding of programming (like Python or C#) and cloud concepts is recommended. This course is a technical "Associate" level program involving API integrations and framework orchestration.
Agentic AI refers to systems that don't just "talk" but "act." You will learn to build agents that can use tools (like searching the web or updating a database) to solve problems without constant human intervention.
You will gain hands-on experience with the industry's most in-demand 2026 frameworks, including Semantic Kernel, AutoGen, and Azure AI Prompt Flow.
Adian Solutions provides 100% coverage of the Microsoft AI-103 exam objectives. Our curriculum is updated in real-time to match the latest 2026 exam blueprint provided by Microsoft.
Yes. You will complete an "Enterprise Multi-Agent Capstone." You will build a team of specialized agents that collaborate to solve a real-world business problem, which serves as a major piece for your professional portfolio.
As an Associate-level Agent Developer, our graduates are currently targeting elite placements in the range of 38 LPA to 45 LPA+, depending on their prior experience and project quality.
The exam is 100 minutes long and includes a mix of multiple-choice questions, case studies, and lab-based tasks where you must configure AI services in a live environment.
Unlike Fundamentals, Associate certifications require annual renewal. However, Microsoft provides a free online assessment to renew your certification, and Adian Solutions provides the updated study material for this renewal.
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Partnering with Adian Soft Solutions transformed our AI adoption journey. Their training programs gave our team the confidence to deploy advanced ML models in production. The hands-on labs and real-world case studies were game changers.
Priya Sharma, CTO Global Tech Innovators