AI for Developers: from issue to reviewed change
Establish a dependable AI-assisted development workflow from issue to tested, reviewed change.
Developers practise how to give coding tools the right repository context, plan changes, inspect generated work, run tests and leave evidence for reviewers. The focus is team reliability, not faster typing.
Recommended format
Core workshop · 4–6 hours
Delivery
On-site / in person, online, or hybrid
01 · Best for
Is this the right workshop?
Software engineers, technical leads, platform teams and product engineers using or evaluating coding assistants and agents.
Not the best fit when
- — You need a finished software implementation rather than team training.
- — You want a passive keynote without practical work.
- — You require a legal or compliance guarantee.
02 · What changes after the workshop
What this program changes in practice
Coding assistants can produce a convincing change before they understand the repository, the reason behind the issue or the tests that protect the system. That is why the workshop treats AI-assisted development as a team operating practice rather than a collection of shortcuts. Developers learn to provide useful context, require a plan, inspect the diff, run the right tests and leave evidence that makes the eventual review easier rather than more uncertain.
The session is not a fixed sequence of demonstrations. The facilitator works with the team on Prepare repository context, Issue to plan, Plan to change, using GitHub Copilot, Codex & coding agents, Agent Design Canvas, Your approved AI tools when those systems are approved and available. Participants compare first results, make missing context visible and improve the work together. They learn not only which request works, but why an output is reliable enough to move into the next working step.
The value should not end when people leave the room. Exercises therefore produce concrete outputs such as Repository instruction template, Agent-ready development workflow, Test-evidence checklist. They give colleagues a traceable starting point, show where human review is still required and make the next pilot smaller and more realistic. Optional follow-up work can build on those artefacts instead of beginning again with another general introduction.
- Give coding tools useful repository context
- Move from issue to explicit implementation plan
- Require tests and review evidence from agent work
- Agree team boundaries, permissions and standards
03 · Tools your team will use
Tools your team will use
- GitHub Copilot, Codex & coding agents
- Move from issue to plan, change, tests and review inside the team’s approved development environment.
- Agent Design Canvas
- Define the job, sources, tools, permissions, decisions and human checkpoints before building an agent.
- Your approved AI tools
- Exercises adapt to the systems, licences and data boundaries your organisation has approved.
04 · From uncertainty to a shared way of working
Establish a dependable AI-assisted development workflow from issue to tested, reviewed change.
- Before
- Developers use different assistants in different ways, while repository context, permissions, tests and review evidence remain inconsistent.
- In the room
- The team uses GitHub Copilot, Codex & coding agents, Agent Design Canvas, Your approved AI tools on its own examples. Every exercise ends with checking, improvement and a clear human decision.
- The day after
- Participants leave with more than notes: Repository instruction template, Agent-ready development workflow, Test-evidence checklist and an agreed next step.
05 · A practical syllabus
A practical syllabus
01 Prepare repository context
Create instructions, architecture notes and constraints the tool can follow.
Working output: Repository instruction template
02 Issue to plan
Turn an ambiguous request into a reviewable implementation plan.
Working output: Agent-ready development workflow
03 Plan to change
Make focused edits, inspect diffs and keep the developer in control.
Working output: Test-evidence checklist
04 Tests, security and review
Require evidence, test failure paths and examine security-sensitive changes.
Working output: Team standard and pilot backlog
05 Team operating standard
Define approved tools, permissions, review rules and a pilot backlog.
Working output: Repository instruction template
06 · What your team takes away
A prepared workshop, not a generic presentation
- Repository instruction template
- Agent-ready development workflow
- Test-evidence checklist
- Team standard and pilot backlog
Optional follow-up clinics and adoption support can be added after the workshop.
- • Sponsor alignment call
- • Short participant questionnaire
- • Examples adapted to your work
- • Facilitated live practice
- • Digital resources
- • Concise facilitator summary
07 · Delivery options
One workshop, three ways to take part
The learning goals, practical exercises and take-away resources stay consistent. We adapt the room, collaboration tools and facilitation rhythm to the way your team is joining.
- On-site / in person
- Delivered at your organisation or an agreed venue, with the facilitator and participants working together in the room.
- Online
- A fully live, facilitated workshop using an agreed video platform, shared exercises and structured small-group work.
- Hybrid
- Designed for a team split between the room and remote participants, with activities adapted so both groups can contribute and receive support.
08 · Facilitated by a practitioner
Facilitated by a practitioner
George Raymond Alchoufi combines software engineering, facilitation and executive coaching. The session is adapted to your team’s work, approved tools and review responsibilities.
Program content reviewed July 2026
Delivered across Switzerland
Available on-site in Zürich, Geneva, Lausanne, Basel, Bern and other Swiss locations by arrangement, with hybrid delivery for distributed teams.
Continue your team’s learning
Questions before booking
What will our team achieve in the AI for Developers workshop?
Establish a dependable AI-assisted development workflow from issue to tested, reviewed change. The practical outcomes are: Give coding tools useful repository context; Move from issue to explicit implementation plan; Require tests and review evidence from agent work; Agree team boundaries, permissions and standards.
Who is the AI for Developers workshop designed for?
Software engineers, technical leads, platform teams and product engineers using or evaluating coding assistants and agents.
Which tools will participants use?
The practical stack includes GitHub Copilot, Codex & coding agents, Agent Design Canvas, Your approved AI tools. Exercises are adapted to the licences and systems your organisation has approved.
How long is the workshop and how is it delivered?
The recommended format is Core workshop · 4–6 hours. Every core workshop is designed to stay within four to six hours; alternatives include Focused workshop · 4 hours, Optional follow-up clinic · 60–90 minutes. Choose an in-person session at your organisation or an agreed venue, a fully online workshop, or a hybrid format. Delivery is available in English, French or German.
What preparation is required?
No technical prerequisite is required. We hold a short sponsor alignment call and participant questionnaire, then adapt examples to approved tools and non-sensitive work.
What does the team take away?
The working package includes Repository instruction template, Agent-ready development workflow, Test-evidence checklist, Team standard and pilot backlog, digital resources and a concise facilitator summary.
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