Iasa AI Architecture Master Class CITA-A

  • Start Date:
  • Duration: 4 days full-time
  • Delivery: In-person (Dublin)
  • Subsidised Fees: Please enquire

Iasa AI Architecture Master Class CITA-A

Part-funded

AI architecture training for enterprise software architects, solution architects and technology leaders

For software architects, solution architects and senior technology professionals, the challenge is no longer simply understanding AI as a concept. It is knowing how to design AI-enabled systems that are secure, scalable, resilient, governed and aligned to real business needs.

The Iasa AI Architecture Master Class [CITA-A] is a four-day, in-person AI architecture course in Dublin for experienced software architects, solution architects and senior technology professionals who need to design governed, secure and scalable AI-enabled systems. The course explores the principles, patterns, tools and practices needed to move from traditional software and solution architecture into the emerging world of artificial intelligence architecture.

Rather than focusing on coding, participants will examine key areas including AI foundations, prompt engineering, AI software architecture, LLMOps, AI governance, agentic AI, MCP, A2A, data management and quality attributes. The programme also uses case study work, facilitated discussion and applied architecture exercises to help learners connect the theory to real project environments.

Developed by architects, with architects and for architects, this course supports professionals who want to build AI-first architectural thinking and progress towards the globally recognised CITA-A Certified IT Architect certification pathway.

This Technology Ireland ICT Skillnet programme is cofunded by the Government of Ireland and the European Union. Click here to learn more about how the EU provides funding for a broad range of projects and programmes in Ireland.

This course is designed for experienced technical professionals who are involved in designing, delivering or leading technology solutions, and who now need to understand how artificial intelligence changes the architecture landscape.

Following the course, these professionals will be confident when taking the best practices and lessons learned in this course directly back to their respective organisations to deliver an immediate impact.

Ideal candidates for the course are:

  • Software Developers & New Software Architects: Those who have completed large feature sets on 1-5 projects in at least one modern development environment and have participated in design, agile, and DevOps processes.

 

  • Non-Coding Software Practitioners: Individuals with experience performing solution or enterprise architecture and at least one technical specialty (infrastructure, software, information, security) across 1-5 projects.

 

  • Technology Leaders: Technology leaders may also benefit from the programme, particularly CTOs, CISOs, DevOps leaders, programme managers, project managers and senior technical decision-makers who need to understand the architectural implications of AI adoption across products, teams and organisations.

 

This course requires general familiarity with development tools, design, agile, and DevOps processes. You should have basic knowledge and experience of system development and AI. The course does not teach how to code.

 

The programme is structured as a practical AI architecture journey. Learners move from AI foundations and model interaction through to prompt engineering, AI software architecture, LLMOps, governance, agentic AI, data readiness, quality attributes and applied architecture presentations. The curriculum consists of an introduction, followed by 14 modules and a closeout session

Phase 1
AI foundations and prompt engineering
Phase 2
Architecture, LLMOps and governance
Phase 3
Agents, MCP, A2A and versioning
Phase 4
Data, quality attributes and applied design
Module 01

AI Foundation

Lecture Topics

  • Introduction to AI
  • Impact of AI on our business
  • Architectural overview
  • Understanding AI topics
 Facilitated Peer Discussion

  • Architecture use of patterns
  • Discuss how the IASA repository matches and can be utilised
Module 02

AI Foundation Continuation

Lecture Topics

  • Selecting a model
  • Interacting with data
  • Agentic AI introduction
  • Software engineering with AI
 Facilitated Peer Discussion

  • Discuss the trade-offs
  • Discuss what is required with artificial intelligence
  • Discuss using an existing model versus creating your own model
Module 03

Prompt Engineering

Lecture Topics

  • Prompt engineering
  • Prompt frameworks
  • Grounding
  • RAG
 Facilitated Peer Discussion

  • Discuss prompt engineering activities
  • Discuss the frameworks and how they benefit your interaction with models
  • Discuss using your own data
Module 04

AI Software Architecture

Lecture Topics

  • Describe software architectures to be used with AI
  • Describe the capabilities of AI
  • Describe views and viewpoints in AI design
 Facilitated Peer Discussion

  • Discuss AI capabilities and model types
  • Discuss the skills and knowledge needed by different roles on a team
  • Discuss the hype cycle and how it impacts the industry
Module 05

LLMOps

Lecture Topics

  • Understanding LLMOps
  • Understanding the LLMOps lifecycle
  • Describing evaluations
  • Walking through monitoring and automation
 Facilitated Peer Discussion

  • Discuss the similarities and differences between DevOps and LLMOps
  • Discuss the changes you need to be aware of in the LLMOps lifecycle
  • Discuss evaluations and why they are needed
  • Discuss monitoring and automation in your solutions
Module 06

AI Governance

Lecture Topics

  • Outline AI governance
  • Describe governance principles
  • Describe model governance
  • Describe operations governance
 Facilitated Peer Discussion

  • Discussion about creating AI governance
  • Discussion on differences between existing governance activities and those required of AI
  • Discussion on operations governance
  • Discussion of creating your own governance model
Module 07

Agents and Agentic AI

Lecture Topics

  • Learn the different types of agent AI patterns
  • Understand how and why Domain Driven Design is so important in Agentic AI
  • Describe the risks and safeguards required
 Facilitated Peer Discussion

  • Discussion on Agentic AI compared with other AI patterns
  • Discussion on patterns
  • Discuss the need and benefits of Domain Driven Design
  • Discussion on risks and safeguards
Module 08

Agents and MCP

Lecture Topics

  • Understanding current challenges
  • Understanding MCP
  • Understanding limitations with MCP
  • MCP security
  • Understanding AI Gateway services
 Facilitated Peer Discussion

  • Discuss the challenges that brought about the need for MCP
  • Discussion on performance topics for MCP
  • Discussion on MCP security
Module 09

Agents and A2A

Lecture Topics

  • Understanding A2A
  • Understanding the implementation and use of A2A
  • Understanding A2A security
 Facilitated Peer Discussion

  • Discuss A2A and where and when to use it
  • Discussion of differences between A2A and MCP
  • Discussion on A2A security
Module 10

Agent Versioning

Lecture Topics

  • Learn about agent versioning
  • Understand what causes versioning activities
  • Outline agentic behaviours
  • Understand versioning strategies
  • Understand versioning recommendations
 Facilitated Peer Discussion

  • Discuss agent versioning techniques
  • Discuss what causes versioning events
  • Discuss agentic behaviours as well as the strategies for versioning
Module 11

Data Management

Lecture Topics

  • Understand legacy data challenges
  • Understand how to create a trusted data foundation
  • Understand data estate architecture and transformation
  • Understand handling structured versus unstructured data
  • Understand what is required for data readiness
 Facilitated Peer Discussion

  • Discuss challenges surrounding legacy data process and technologies
  • Discuss data estate architectures and uses
  • Discuss the topics required for data estate transformations
  • Discuss trade-off and data readiness activities
Module 12

Quality Attributes

Lecture Topics

  • Presentation of quality attribute topics
  • Understanding how all quality attributes are required for an AI implementation
  • Understanding performance and operations requirements and activities
  • Understanding the application layer architecture
 Facilitated Peer Discussion

  • Discuss reliability, security, cost optimisation, operational excellence and performance topics
  • Discuss observability and monitoring as well as guidelines for implementation
Module 13

Case Study

Lecture Topics

  • Presentation of the case study
  • Understanding how ML and AI were utilised
  • Understanding real-world privacy in the case study
  • Review a competitor and the implementation
 Facilitated Peer Discussion

  • How AI was used in both scenarios
  • Discussion on the technologies and their implementation
  • How the implementation brings together all of the topics in this course
Module 14

Final Presentations

Lecture Topics

  • Present a review of material
  • Review all the design cards put together by the students
  • Wrap-up and discussion
 Facilitated Peer Discussion

  • Discuss the design implications and impacts in creating an AI solution
Closeout Module

Course Review and Next Steps

Lecture Topics

  • Review of the topics that were covered
  • Discuss the next steps in your learning path
 Facilitated Peer Discussion

  • Final review of the course
  • Discussion on the interactive activities in the course

 

This course is delivered through instructor-led sessions, facilitated peer discussion, applied group work and practical architecture exercises. Participants will use collaborative tools such as Miro and Microsoft Teams to complete classwork, access course materials and develop architecture outputs.

The master class is led by Stephen Kaufman, Chief Architect in the Americas Office of the CTO for Microsoft, bringing extensive enterprise AI and architecture experience into the learning environment. Scroll down to learn more about Stephen and watch his lecturer interview.

The course emphasises immersive, hands-on application. Each section utilises Canvas and Cards (such as Architecture Definition, SWOT, PESTEL, and Business Model Canvases) to ground theory into practical workspaces. Students must also ensure their devices can access Miro (for diagramming), MS Teams (for collaboration), and Chronus (for mentoring).

Learners should expect to complete practical exercises between sessions. The programme includes workshop-based learning and final presentation activity, helping participants apply the tools, techniques and architecture concepts to either a provided case study or their own professional project context.

 

  • Duration: 4 days (4-5 lessons / day)
  • Time commitment: Daily full-time
  • Methods: 45min presentation / 45min workshop per lesson
  • Platforms: Miro and MS Teams

 

 

On completion of this course, participants will have developed practical skills and architectural understanding that can be applied directly to AI-enabled projects and digital transformation initiatives.

  • Design AI-enabled architectures that align technology choices with business goals, product needs and customer value.
  • Assess AI models, data requirements and trade-offs to make informed AI solution design decisions.
  • Apply prompt engineering, grounding and RAG principles to improve AI interactions with enterprise data.
  • Understand LLMOps practices across evaluation, monitoring, automation, lifecycle management and operational readiness.
  • Build governance-aware AI solutions that address security, risk, privacy, model oversight and responsible adoption.
  • Architect agentic AI systems using agent patterns, MCP, A2A, versioning, safeguards and quality attributes.

 

 

This programme supports participants in progressing towards the CITA-A Certified IT Architect certification pathway. Assessment is based on practical application of course concepts, workshop activity and final presentation work. Participants will apply AI architecture tools and techniques through case study exercises or relevant professional project work.

 

About Iasa CITA-A Certification

The Iasa CITA-A (Certified Information Technology Architect – Associate) is a globally recognised, vendor-independent certification for mid-career architects focusing on specialised domains, including solution, software, and AI architecture. It proves your competency in applying the ITABoK (Information Technology Architecture Body of Knowledge) to complex real-world scenarios.

 

Fees

The full course fees are €2,000.

Eligible applicants* can access reduced course fees through Technology Ireland ICT Skillnet funding.

Please use the Make an Enquiry form on this page to speak to our team about access to funded course fees.

 

*Funding Eligibility

Applicants must be employed by, or operating as, an eligible business engaged in economic activity in the Republic of Ireland. This includes private companies, sole traders, commercial social enterprises and eligible commercial semi-state organisations. Additional eligibility verification may be required for certain organisation types to avail of grant-aided fees.

Applicants who do not meet our funding criteria may in some cases be able to apply directly with Iasa Ireland and pay the full course fee of €2,000 if there are available places.

 

If you have any questions for our programme team or would like to apply for the course, please use the ‘Make An Enquiry’ form on this page.

MEET YOUR LECTURER

Learn from enterprise AI architecture expertise

Explore AI architecture through the experience of a practitioner working at the intersection of enterprise architecture, cloud transformation and emerging AI technologies.

Course Lecturer Video

Stephen Kaufman

Chief Architect in the Americas Office of the CTO for Microsoft

Recorded in Dublin, July 2026

Stephen specialises in enterprise AI strategy, agentic AI architectures and large-scale cloud transformation. He partners with global brands across multiple industries to modernise data platforms, implement responsible AI governance and design intelligent systems that work reliably in real business environments.

Stephen is recognised for his ability to translate complex AI concepts into strategic, actionable roadmaps for executives, engineering teams and developers alike. His work spans hands-on architecture, executive storytelling, governance design and thought leadership.

Enterprise AI Architecture AI architecture, agentic systems, governance and responsible implementation.
Applied Technology Leadership Translating emerging AI technologies into practical enterprise architecture decisions.

Frequently Asked Questions

The questions our team are asked most often about this programme. If your question is not answered here, make an enquiry and we will come back to you.

The programme is designed for experienced technology professionals involved in designing, delivering or leading technology solutions. It is particularly relevant for software developers moving into architecture, software and solution architects, enterprise architects, and senior technology leaders who need to understand how AI changes architecture decisions across products, systems and organisations.

Not necessarily. The programme is suitable for practising architects as well as experienced software developers and other technical professionals progressing towards architecture roles. Participants should, however, already have practical exposure to modern development environments and be familiar with areas such as system design, Agile and DevOps.

You should have basic knowledge and experience of AI and system development before joining the programme. The course introduces important AI foundations, but it is not an introductory AI course. It is designed to build on an existing technical foundation and focus on the architectural implications of designing and operating AI-enabled systems.

The course does not teach coding, and participants are not attending to learn how to build AI applications line by line. However, general familiarity with development tools, software design, Agile, DevOps and system development is expected so that participants can engage with the technical architecture concepts and trade-offs explored throughout the programme.

The programme covers AI foundations, model selection, prompt engineering, grounding and RAG, AI software architecture, LLMOps, AI governance, agentic AI, Model Context Protocol (MCP), Agent-to-Agent (A2A), agent versioning, data readiness and AI quality attributes. It also explores security, privacy, monitoring, operational performance and the architectural trade-offs involved in enterprise AI systems.

This programme is focused specifically on architecting AI-enabled systems, rather than simply understanding or using AI tools. Participants examine how AI affects software architecture, data, governance, security, operations, reliability and system design, with a particular focus on making architecture decisions that support real business, product and customer requirements.

The programme has a strong practical and workshop-based approach. Participants combine instructor-led learning with peer discussion, group work, case studies and applied architecture exercises. Tools such as Miro, architecture canvases and design cards are used to apply concepts to either a provided case study or a relevant professional project context.

Participants will be better equipped to design AI-enabled architectures aligned with business goals, assess AI models and data requirements, apply RAG and grounding principles, understand LLMOps, incorporate governance and responsible AI controls, and design agentic systems using patterns including MCP and A2A. The programme is designed so these capabilities can be applied directly to AI projects and digital transformation initiatives.

CITA-A stands for Certified Information Technology Architect – Associate. It is a globally recognised, vendor-independent Iasa certification aimed at mid-career architects working across specialised areas including solution, software and AI architecture. The AI Architecture Master Class supports participants in progressing through the CITA-A certification pathway and applying the Iasa ITABoK to real-world architecture scenarios.

The full course fee is €2,000. Eligible participants can access reduced fees through Technology Ireland ICT Skillnet funding. To qualify for subsidised fees, applicants must be employed by, or operating as, an eligible business engaged in economic activity in the Republic of Ireland. This includes private companies, sole traders, commercial social enterprises and eligible commercial semi-state organisations. Additional eligibility verification may be required for certain organisation types to avail of grant-aided fees.

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