Agentic engineering training
Help your developers move beyond individual experiments with chat bots, autocomplete and coding agents such as Codex or Claude Code. We give teams a shared, safe and repeatable way to use AI inside a professional engineering process.
AI use often grows unevenly. Some developers build effective personal workflows while others rely on occasional browser chat or autocomplete. The organisation has no common approach to quality, security, architecture or review.
Our programme helps the team agree how coding agents fit into delivery. Developers remain responsible for understanding, reviewing, testing and owning the code they ship. The agent works inside those standards, with clear goals and checks at every stage.
OUTCOMES
Teams learn where agents can help with implementation, unfamiliar code, debugging, refactoring, documentation and technical analysis. They also establish the controls that keep AI-assisted work aligned with the architecture and easy to review.
THE PROGRAMME
These themes provide a practical framework rather than a fixed syllabus. We adapt the examples and depth to your team’s tools, codebase, development lifecycle and current level of AI adoption.
We establish a shared understanding of the tools and the responsibilities that come with using them on real development work.
The focus then moves to the habits that make day-to-day agent use useful, controlled and repeatable.
Individual techniques become a practical engineering operating model that the whole team can understand and improve.
Sessions use realistic engineering work, live demonstrations and questions from the developers in the room. Your team’s real concerns, conventions and delivery process can shape the discussion, without requiring an immediate change of language, platform, IDE or AI vendor.
The core programme has been delivered across three connected sessions, giving people time to apply the ideas between discussions. We can adapt the structure and depth to the team and the outcomes you need.
Practical outputs can include:
DabApps has spent more than 15 years building and maintaining complex software products. We now use AI deeply within our own engineering practice, while keeping the same expectations around design, testing, security, code review and long-term maintainability.
Our engineers apply coding agents to code review, testing, debugging, refactoring, documentation, architecture exploration and delivery workflows. That practical experience lets us connect the tools to real questions about software quality, service boundaries, technical debt and team accountability.
We can explain what is technically possible, demonstrate how the work fits together and help your team put it into practice. For broader questions about where AI belongs in your organisation or products, explore our AI consultancy.
CTO & Co-Founder
Jamie has worked in software development for more than 20 years. He is an accomplished systems designer with a particular interest in efficient development practices, codebase structure and the day-to-day experience of engineering teams.
He is an experienced conference speaker and trainer, and writes about testing, software architecture, production AI and why coding agents work better with predictable codebases. As DabApps’ CTO, he brings those ideas back to real delivery: how teams review changes, protect quality and use agents without losing engineering ownership.
It is designed for software development teams and the CTOs, engineering leaders and development managers responsible for how they work. It suits teams starting to adopt AI as well as teams that already have a few experienced individual users.
No. The principles apply across languages, codebases, editors and AI vendors. We can demonstrate modern coding agents such as Codex or Claude Code, while keeping the programme focused on transferable engineering practices.
Yes. Existing experience gives us useful material to work with. We help the team compare current approaches, identify gaps and agree practices that work beyond individual preferences.
Yes. We use your delivery process, tools, conventions and concerns as inputs. The aim is to strengthen the way your team already works, with practical changes that can be adopted and reviewed over time.
It is practical and discussion-led. Sessions use realistic engineering examples and demonstrations, with space to examine questions from your own projects, codebases and development lifecycle.