AI-Assisted DevelopmentCOURSES

From Assisted to Agentic AI Development

Unlock the full potential of AI-assisted development with a course designed for real-world impact, offering hands-on, practical training built for software engineers and engineering leaders.

This course is designed to accelerate your adoption of AI for engineering, helping realise team and individual benefits earlier. The course focuses on developing high-quality, real-life software products using techniques designed to empower teams, reduce toil, increase quality and fundamentally change the way you work with AI.

Beyond learning AI tools, the course focuses on developing a resilient approach to navigating the evolving landscape of AI in software development and building confidence and capability while working side-by-side with AI.

This course assumes a basic understanding of AI models and tools (eg. prior use of ChatGPT, Claude, etc) and a foundational knowledge of software engineering practices, including test automation.

Check out the course outline below, request more information via the form and book using the links below.

Course structure

  • Nine modules over nine weeks
  • 1.5-hour live interactive sessions per week covering each module
  • Approximately two-to-four hours on self-directed exercises per week based on experience

Course costs

  • $2990 – For the full nine modules over nine weeks. This includes a complimentary introductory module to help you prepare for the course, as well as an optional module to support you in leading change within your organisation. You keep access to all the content for 18 weeks, allowing you to catch up on any live instruction or repeat any part of the course within that time
  • $250 p/h – Optional one-on-one support is available on an hourly basis at any time

Why choose this course?

  • Carve out time and a safe space to learn amongst a cohort of peers
  • Enjoy a curated learning journey designed to help devs accelerate their adoption of real-world AI engineering practices
  • Apply practical and relevant patterns to work after the first week
  • Accelerate your career as an early adopter of AI for engineering
  • Position yourself as a leader within your organisation
  • Learn from deeply experienced trainers and software practitioners
  • Experience the joy of paying down technical debt with assisted and agentic AI
  • Bring your IDE, coding style and preferences as you learn

Book now

Course outline

Our AI-Assisted Development course is a nine-week, cohort-based learning journey structured across three progressive phases. The programme enables participants to move from collaborating with AI tools to building orchestrated, autonomous AI systems.

With hands-on projects and real-world examples, each module compounds skills, tools and design patterns for next-generation software development.

Accelerate your career via

  • Live cohort-based online workshops
  • Weekly expert-led sessions and async activities
  • Ongoing discussions with instructors and other learners
  • Capstone projects with peer review

Optional introduction – Get Ready

  • Overview of the AI-assisted course journey
  • Introduction to course concepts and projects
  • Philosophy and pedagogy applied to the course design

Phase 1 – AI Collaboration Fundamentals (weeks 1-3)

Module 1: Foundations of AI Collaboration

  • AI personas and prompting techniques
  • Tools overview: Cursor, Cline, Claude Code and more
  • Balancing control, delegation and overwhelm
  • Project: Configuration HTTP API Service

Module 2: Building Context

  • Document-driven development techniques
  • Managing context and the context window
  • Multi-turn, structured planning sessions
  • Project: Quality-enhanced Microservice

Module 3: Feedback Loops and Dynamic Context

  • Creating and using reusable assets and context
  • Creating a positive feedback loop using command responses
  • Combining static and dynamic context
  • Project: New API features built from context

Phase 2 – Building AI That Can Act (weeks 4–6)

Module 4: Teaching Assistants to Use Tools

  • Understanding MCP architecture and protocols
  • Configuring and using existing MCP servers
  • Testing and experimenting with tool combinations
  • Project: Set up a local MCP ecosystem with four-five servers

Module 5: Model Context Protocol (MCP)

  • Implementing MCP servers from scratch
  • Wrapping existing servers (your Configuration API)
  • Error handling and production patterns
  • Project: Build and deploy a custom MCP server

Module 6: Tool Orchestration and Coordination

  • Sequential orchestration: Chaining MCP tools for complex workflows
  • Parallel coordination: Running multiple tools simultaneously
  • Conditional logic: Dynamic tool selection based on context
  • Error handling: Fallback strategies and recovery patterns
  • Observability: Monitoring multi-tool operations
  • Project: Coordinate multiple MCP servers to automate a development workflow

Phase 3 – Orchestrating Intelligent Systems (weeks 7–9)

Module 7: Building Agents

  • Agent architecture patterns
  • Agent debugging and observability
  • Testing strategies for agent behaviour
  • Platforms, tools and technologies
  • Project: Build single-purpose agent with comprehensive testing

Module 8: Multi-Agent Coordination

  • Agent communication patterns
  • Specialisation and role-based design
  • Shared memory and oversight
  • Conflict resolution between agents
  • Project: Create a multi-agent development workflow

Module 9: Agent Observability and Performance Tuning

  • Monitor and measure agent system performance
  • Optimise agent decision-making and efficiency
  • Build feedback loops for agent improvement
  • Project: DORA and Flow Metrics Dashboard

Bonus Module – Humane AI Adoption and Change

  • Lead with empathy through transformation
  • Change management principles
  • Addressing adoption objections
  • Building communication matrices and plans

About the instructor

  • The course is taught by Don Smith, an instructor with over 30 years’ international experience in software development, course design and technology education, including in the US with Microsoft, and the establishment of the successful Dev Academy in New Zealand. His expertise is rooted in real-life problem-solving for clients, ensuring the content is practical and relevant.

Benefits

FOR LEADERS AND DECISION-MAKERS

  • Supports strategic adoption: This course helps your team confidently adopt and integrate AI tools into their workflows in a safe, curated environment – our approach complements our recommendations for the successful introduction of these tools organisation-wide (available as an optional module)
  • Time and cost savings from curated learning path: Help your team avoid uncoordinated trial and error, saving significant time and resources in their AI learning journey. The course cost will be easily recouped through increased productivity and higher quality outcomes
  • Deep technical expertise: This is a deep engineering and technical course, not a superficial overview. Your team will learn practical patterns they can apply to their work immediately
  • Enhanced quality and reduced toil: While speed is a benefit, our focus is on fundamentally improving code quality, increasing flow and reducing developer toil, which are key drivers of developer happiness and retention
  • Leadership development: This course equips your team members to become internal leaders and champions of AI adoption within your organisation

FOR COURSE ATTENDEES

  • Understand the ‘why’: Through the course learning objectives, we address how AI engineering practices improve individual and team flow, reduce technical debt and improve quality. We also explore ways to mitigate the potential risks and costs associated with adoption
  • Pedagogy and approach: Our unique teaching method focuses on building an intuition around AI tools and their application, rather than just providing step-by-step instructions
  • Resilience to change: You will learn a framework for understanding and adapting to future changes in AI, ensuring your learning remains relevant and your skills resilient
  • Skills relevance: Combine your software engineering experience with AI skills to stay relevant in the market and maintain job security
  • Beyond code completion: This course goes far beyond basic code completion, teaching you how to learn and use it for resilience in the changes coming to software development practices
  • Addresses AI-coding myths and acknowledges realities:
  • Myth: Senior devs are more productive without it
  • Myth: AI-assisted software development is all about speed
  • Myth: AI coding tools are just spicy autocomplete that speed up typing
  • Myth: To develop software with AI support means you must “vibe-code”
  • Reality: You won’t always get a productive experience “straight out of the box” with AI-coding tools. There are many practices and constraints to learn
  • Reality: You can risk ceding too much control to the models if you’re not careful
  • Reality: Things are moving at such a rapid pace that you cannot pin down ‘best practices’ but rather must identify concepts and knowledge you can use to help apply judgment as things evolve
  • Practical application: The course emphasises building a set of practices for successfully leveraging AI tools. You’ll learn to identify and apply emerging patterns for effective AI-assisted development deliberately
  • Flexible learning: While there will be dedicated live lecture slots, video recordings will be available for flexible viewing at your convenience. Similarly, capstone project exercises can be adapted to your schedule

FAQs

COURSE OVERVIEW AND AUDIENCE

Who is this course designed for? This course is perfect for software engineers working in teams, tech leads and architects, heads of engineering, AI champions driving developer enablement, any AI enthusiast ready to expand their skillset and curious sceptics of AI coding assistants who want to see what’s possible.

Do I need prior AI experience to take this course? Basic AI experience with LLMs is required. The course takes you from basic AI collaboration to building fully autonomous systems, making it accessible whether you’re new to AI or looking to deepen your expertise.

Is this course suitable for engineering managers? Absolutely. Heads of engineering and tech leads will gain valuable insights into AI adoption strategies and change management, helping them guide their teams through AI integration effectively.

LEARNING OUTCOMES AND STRUCTURE

What will I learn in the From Assisted to Agentic AI Development course? This course takes you through a hands-on journey from basic AI collaboration to building fully autonomous AI systems. In practical terms, you’ll learn how to build production-ready software with AI assistance, automate development workflows using AI tools and memory techniques and design multi-agent AI systems that can work together to solve problems.

The curriculum is organised in three phases and, as you progress, you’ll gain skills in prompt engineering, tool integration (like function calling and retrieval systems) and orchestrating intelligent agents. By the end, participants even learn how to lead AI adoption in their organisations, combining the technical know-how with change management principles for introducing AI into teams.

What kind of projects will I work on? You’ll build several real-world projects including a multi-tenanted microservice with AI-assisted lifecycle (including planning and documentation), a domain-specific assistant with RAG and memory, automated dev workflows and AI reviews and research and writing agents collaborating at scale. Along the way, you’ll build real projects which will give you portfolio pieces and experience with AI in real-world scenarios.

Will these projects be portfolio-worthy? Definitely. All projects are designed to give you tangible portfolio pieces and real-world experience with AI in software development scenarios.

UNDERSTANDING AI DEVELOPMENT CONCEPTS

What is ‘AI-assisted development’? AI-assisted development refers to using AI tools, such as Claude Code, to help software developers write, review and optimise code more efficiently. These tools leverage machine learning models trained on codebases to make recommendations.

When combined with high-quality examples and relevant context, these tools can generate code, tests, infrastructure and other important artefacts, speeding up development while improving quality.

What does ‘agentic AI development’ mean? Agentic AI development is about creating AI systems (or ‘AI agents’) that act with autonomy and initiative, rather than just following a single prompt or rule. In practice, agentic AI systems can make independent decisions and perform multi-step tasks to achieve goals with appropriate oversight.

This means moving beyond using AI as a passive assistant. Developers can build orchestrated, autonomous AI agents that plan, execute and adapt to complete complex objectives independently, provided deterministic guardrails are in place to check results.

BENEFITS AND IMPACT

How can AI tools benefit software developers in coding? AI tools can dramatically boost a developer's productivity and quality by reducing toil, accelerating the implementation of known patterns and providing intelligent search and guidance.

For instance, AI coding assistants can accelerate prototyping, generate code based on appropriate patterns, recommend and apply refactorings to reduce technical debt, help with code analysis, accelerate code migration, generate high-quality documentation, coach junior developers and broaden individuals' skills, allowing for teams to be more cross-functional and autonomous.

When used effectively, the result is faster and higher-quality software, allowing more time for humans to focus on creative and complex aspects of software design and delivery.

How will this course impact my daily development work? You’ll dramatically boost your efficiency by learning to automate tedious coding tasks and leverage AI for intelligent guidance. The skills you learn will help you focus more on creative and complex aspects of software design.

ORGANISATIONAL BENEFITS AND ADOPTION

Why should tech leads and engineering managers invest in AI development training for their teams? Adopting AI in the development process is quickly becoming a priority for organisations, with most tech leaders planning on expanding the use of AI in their software teams in the near term.

By investing in AI engineering training, tech leads and engineering managers ensure their teams stay ahead of the curve and can fully leverage AI tools for productivity and innovation. A structured programme equips engineers to build production-ready AI systems and even lead AI adoption and change management inside teams.

Training developers in AI improves day-to-day efficiency and empowers your engineers to drive the company’s AI strategy effectively.

How can companies support AI adoption in their software development teams? Companies can support AI adoption by upskilling their developers and fostering a culture of psychological safety where engineers can experiment and learn new techniques, combined with targeted training courses.

Cohort-based training helps engineers learn how to use AI tools and build agentic systems effectively in small groups, rather than expecting them to figure it out independently. It’s also important to designate AI champions or leaders who can drive developer enablement, experts who advocate for AI use, share best practices and mentor others.

From a change management perspective, leadership should introduce AI with empathy, addressing developers’ concerns (like fear of job impact or trust in AI outputs) and clearly communicating the benefits and goals of using AI.

By creating an environment that encourages experimentation with AI, provides the necessary tools and addresses challenges, leaders can create the conditions for AI adoption and accelerate benefits.