- Choose an appropriate model for each task, including LLMs, small language models, multimodal models, and Foundry Tools.
- Choose Foundry services for generative tasks, grounding, vector search, agent workflows, or multimodal processing.
- Choose an appropriate method for retrieval and indexing.
- Choose memory, tool, and knowledge integration services for agent solutions.
Microsoft Azure AI Apps and Agents Developer Associate (AI-103)
Microsoft Azure AI Apps and Agents Developer Certification Training Course AI-103
Prepare for Exam AI-103: Developing AI Apps and Agents on Azure — the associate credential that replaces AI-102. Accumentum maps the course to Microsoft’s published skills measured as of 16 April 2026.
Course focus: Planning Foundry solutions, generative AI and agents, RAG, computer vision, text analysis, and information extraction — not the retired AI-102 Cognitive Services outline.
Course Overview
Build, manage, and deploy agents and AI solutions on Microsoft Foundry.
Accumentum’s Microsoft Azure AI Apps and Agents Developer Certification Training Course (AI-103) prepares Azure AI engineers for Exam AI-103: Developing AI Apps and Agents on Azure, which leads to Microsoft Certified: Azure AI Apps and Agents Developer Associate. Exam AI-103 replaces AI-102.
Microsoft’s audience is an Azure AI engineer who builds, manages, and deploys agents and AI solutions that take advantage of Microsoft Foundry. Candidates should have experience developing apps by using Python and be familiar with general AI, generative AI, and Azure services.
This page uses Microsoft’s published skills measured as of 16 April 2026. Accumentum does not invent exam prices or unofficial question counts.
Course Objectives
What you will be able to plan, build, and operate.
01
Plan and manage Foundry solutions
Choose models and Foundry services, design infrastructure and CI/CD, and manage quotas, security, and responsible AI.
02
Generative AI and agents
Build RAG applications, tool-augmented flows, and orchestrated multi-agent solutions with the Foundry SDKs.
03
Vision, text, and speech
Implement image and video generation, multimodal understanding, language analysis, and speech as an agent modality.
04
Information extraction
Ingest and index content, configure semantic, hybrid, and vector search, and extract structured content with Content Understanding.
Who Should Attend
- Azure AI engineers who build, manage, and deploy agents and AI solutions with Microsoft Foundry.
- Developers with Python experience who are moving from prebuilt Azure AI services (the AI-102 era) to Foundry apps and agents.
- People who collaborate with stakeholders, architects, data scientists, DevOps, and cloud security engineers on AI solutions.
- Learners still on AI-102-only Cognitive Services material should use this AI-103 outline instead of treating AI-102 as current.
Prerequisites
- Experience developing apps by using Python.
- Familiarity with general AI, generative AI, and Azure services.
- Comfort with Foundry concepts: models, RAG, agents, and responsible AI instrumentation.
- No Accumentum-invented hour counts. Confirm any Microsoft-stated prerequisites on Microsoft Learn.
Pathway Map
Ten modules mapped to Microsoft AI-103 skills as of 16 April 2026.
Skills measured: plan and manage an Azure AI solution 25–30%; implement generative AI and agentic solutions 30–35%; computer vision 10–15%; text analysis 10–15%; information extraction 10–15%.
1
Choose Foundry services for generative AI and agents
2
Set up AI solutions in Foundry
3
Manage, monitor, and secure AI systems
4
Responsible AI across generative and agentic systems
5
Build generative applications with Foundry
6
Build agents with Foundry
7
Optimize and operationalize generative AI systems
8
Implement computer vision solutions
9
Implement text analysis and speech solutions
10
Implement information extraction solutions
Course Content
- Design Azure infrastructure for AI apps and agent-based solutions.
- Choose appropriate deployment options.
- Configure model and agent deployments.
- Integrate Foundry projects with CI/CD pipelines.
- Manage quotas, scaling, rate limits, and cost footprints for model and agent workloads.
- Monitor model performance, drift, safety events, and grounding quality.
- Monitor data ingestion quality, search index health, and relevance performance.
- Configure security, including managed identity, private networking, keyless credentials, and role policies.
- Configure safety filters, guardrails, risk detection, and content moderation.
- Apply responsible AI instrumentation, including evaluators, safety evaluations, and explanation tooling.
- Implement auditing through trace logging, provenance metadata, and approval workflows.
- Govern agent behavior with oversight modes, constraints, and tool-access controls.
- Deploy and consume LLMs, small models, code models, and multimodal models.
- Implement retrieval-augmented generation (RAG) in an application.
- Design workflows, tool-augmented flows, and multistep reasoning pipelines.
- Evaluate models and apps, including fabrications, relevance, quality, and safety, using Foundry SDKs and connectors.
- Define agent roles, goals, conversation tracking, and tool schemas.
- Build agents that integrate retrieval, function-calling, and conversation memory.
- Integrate tools including APIs, knowledge stores, search, content understanding, and custom functions.
- Implement orchestrated multi-agent solutions and autonomous or semiautonomous workflows with safeguards and approval flows.
- Tune generation behavior with prompt engineering and model parameters.
- Implement model reflection, chain-of-thought evaluations, and self-critique loops.
- Set up observability with tracing, token analytics, safety signals, and latency breakdowns.
- Orchestrate multiple models, flows, or hybrid LLM and rules engines.
- Generate images and videos from text prompts and reference media, including inpainting and prompt-driven edits.
- Build multimodal understanding: captions, visual question-answering, alt-text, and Azure Content Understanding.
- Implement video analysis and object or region identification.
- Responsible AI for multimodal content: unsafe visual filters, indirect prompt injection in images, and visual policy rules.
- Extract entities, topics, summaries, and structured JSON using generative prompting and Foundry Tools.
- Detect sentiment, tone, safety issues, and sensitive content; translate with Azure Translator or LLM-powered flows.
- Speech-to-text and text-to-speech for agentic interactions, including custom speech models.
- Enable multimodal reasoning from audio inputs.
- Ingest and index documents, images, audio, and video.
- Configure semantic, hybrid, and vector search for grounding.
- Implement enrichment skills and RAG ingestion including OCR.
- Extract content with multimodal pipelines and Content Understanding analyzers for structured or markdown outputs.
Course Features
Designed for AI-103 Foundry engineer work.
Skills-measured map
Ten modules aligned to Microsoft AI-103 skills measured as of 16 April 2026.
Agent-first associate
Generative AI and agentic solutions are the largest published domain (30–35%) — not the prior AI-102 service catalog.
Public exam facts
Pearson VUE. Microsoft publishes a passing score of 700 or greater. Annual renewal on Microsoft Learn. No invented prices.
Prior exam context
AI-102 is the prior Azure AI Engineer Associate exam. This course is AI-103.
Certification Exam
Prepare for the official Microsoft AI-103 exam.
AI-103 Exam Readiness
Accumentum AI-103 training prepares Azure AI engineers for Developing AI Apps and Agents on Azure.
The Microsoft Certified: Azure AI Apps and Agents Developer Associate exam is AI-103. Public facts from Microsoft: delivered through Pearson VUE; a score of 700 or greater is required to pass; associate certifications expire annually and can be renewed on Microsoft Learn. Accumentum does not publish unofficial question counts or invented exam prices.
Skills measured as of 16 April 2026: plan and manage an Azure AI solution (25–30%); implement generative AI and agentic solutions (30–35%); implement computer vision solutions (10–15%); implement text analysis solutions (10–15%); implement information extraction solutions (10–15%).
Exam AI-103 replaces AI-102. Confirm current scheduling, languages, and interactive-item details on Microsoft Learn and Pearson VUE.
Frequently Asked Questions
AI-103 Azure AI Apps and Agents Developer training with Accumentum: scope, audience, skills measured, and public exam facts.
No. This is Exam AI-103: Developing AI Apps and Agents on Azure, for Microsoft Certified: Azure AI Apps and Agents Developer Associate. AI-102 is the prior exam.
Azure AI engineers who build, manage, and deploy agents and AI solutions with Microsoft Foundry, with Python development experience.
Plan and secure Foundry solutions; build RAG apps and multi-agent systems; implement vision, text, speech, and information extraction on the published AI-103 outline.
Ten modules mapped to Microsoft skills measured as of 16 April 2026, from service choice and responsible AI through agents, vision, text, and extraction.
As of 16 April 2026: plan and manage 25–30%; generative AI and agents 30–35%; vision 10–15%; text analysis 10–15%; information extraction 10–15%.
Pearson VUE. Microsoft publishes a passing score of 700 or greater. Annual renewal on Microsoft Learn. No invented prices or unofficial question counts.
Use Accumentum registration for AI-103 training dates and enrollment support.
Enrollment
Enroll in Microsoft Azure AI Apps and Agents Developer Certification Training (AI-103) with Accumentum.
Enroll in Accumentum’s AI-103 training to prepare for Exam AI-103. For course dates and enrollment support, visit Accumentum’s registration page linked below.
Prepare for AI-103 Foundry apps and agents work.
Associate-level Azure AI engineering on Microsoft Foundry. Not AI-102.