The tools keep changing
New features arrive every week. The team cannot tell which practices will last.
AI training
Give your engineering team a shared way to work with AI
AI tools are already on the desktop. Most teams still lack a practical method. Lynray helps engineers use coding agents on real work. The team keeps technical judgement. Review standards stay clear.
Claude Code | Codex | OpenCode
Plan AI trainingThe adoption problem
Your team has AI tools. The way of working is still unclear.
Individual experiments do not create a reliable engineering practice for the organisation.
Results vary across the team
A few engineers get strong results. Others struggle to apply the same approach.
AI code is hard to trust
Generated code moves quickly. Review and verification do not always keep pace.
There is no shared method
People use different prompts. They use different checks. Learning stays with each person.
A working method
Learn to use AI as a real working method
The training stays practical. Each session connects directly to the way your team ships software.
Practical, not theoretical
The team works through real engineering tasks. The output can be used after the session.
Built for engineering teams
The training fits your stack. It fits your codebase. It fits your review process.
Current, not last year's
The tools change. The training covers planning. It covers context. It also covers review. It covers verification.
Training path
Connect the workshop to real delivery
Each stage creates an output that the team can keep using.
01
Set the baseline
Choose one team. Choose a real workflow. Record the current delivery result.
Evidence: an agreed starting point
02
Train through practice
Engineers plan work with agents. They manage context. They review the result. They verify the change.
Evidence: completed exercises on relevant work
03
Apply the method
The team uses the method in normal delivery. Shared instructions make the workflow repeatable.
Evidence: a working team practice
04
Review the result
Compare the result with the baseline. Keep the practices that improve useful delivery.
Evidence: a clear decision about the next step
Commercial result
Measure delivery improvement after the training
Tool activity is not the result. Valuable software delivered safely is the result.
Lead time
Measure the time from approved work to a safe release.
Review time
Measure how long completed work waits for a useful review.
Rework
Measure how often work returns because the result was incomplete.
Released value
Connect the improved workflow to software that reaches users.
Train teams inside clear enterprise boundaries
Large organisations need common controls before agentic coding expands across repositories.
Training controls
- Approved tools
- Data handling rules
- Repository access rules
- Human review gates
- Secure coding standards
- Clear ownership
What the team keeps
- Shared agentic workflow
- Repository instructions
- Review checklist
- Relevant practice examples
- Delivery baseline
- Next step plan
Current workshops
Choose the right starting point
Each workshop is tailored to your team's needs and current delivery challenges. The session combines focused instruction with hands-on work. Your team leaves with methods and clear next steps it can use immediately.
Agentic Engineering
One-day workshop for developers and architects
Claude Code | Codex | OpenCode
Learn how to use coding agents to deliver production-grade software. Agentic Engineering is a hands-on workshop focused on spec-driven development: you describe precisely what needs to be built and how, the agent implements it, and you retain full technical ownership of the codebase.
You will learn
- Spec-driven development in practice: write tasks that can drive autonomous code generation
- Context management with AGENTS.md, CLAUDE.md, .opencode and repository structure
- Setup and daily use of Claude Code, Codex and OpenCode
- When to let the agent work, when to stop it, and how to catch mistakes early
- How to review and validate AI-generated code with the same rigour as human-written code
- Governance and responsibility in enterprise environments
Who it's for
Developers and architects who want to use AI as a real coding partner without losing technical judgment.
Advanced Agentic Engineering
For teams already using agents in production
Claude Code | Codex | OpenCode
A hands-on follow-up workshop for developers ready to take the next step in adopting agentic engineering across their organisation. We cover advanced context management, loop engineering, Ralph Loops, codebase structuring and multi-agent workflows.
You will learn
- Adversarial development: write tasks so agents review their own work
- Advanced prompting patterns and prompt-writing agents
- Deep context engineering across a real codebase
- Choosing the right model for the task - when cheaper models suffice and when to reach for top-tier ones
- Skills, plugins and multi-agent workflows for shipping faster
Who it's for
Developers and architects with existing experience in agentic development, or who have completed the Agentic Engineering workshop.
Scaling success
Expand after one team shows useful results
A wider programme should follow evidence from normal delivery work.
1
Start with one team
Use a real codebase. Focus on one delivery problem.
2
Standardise what works
Keep the useful instructions. Keep the review rules. Keep the measures.
3
Train the next teams
Adapt the proven method to more repositories. Review the result after each rollout.
Where does your team need a shared AI practice?
Describe the current delivery problem. Lynray will suggest a focused workshop or a wider enablement path.