- Training / EnablementIndustry
- Engineering teamsClient
- Training & EnablementCategory
- Fast to valueTime to impact
The challenge
- Most developers have tried an AI assistant and bounced off it.
- Used naively it produces plausible-but-wrong code; used well it changes how a team ships.
- The gap is skill, not the tool.
Hands-on training that turns developers into effective drivers of AI coding agents.
A practical programme that gets developers genuinely productive with AI coding agents, not just autocomplete, but planning, reviewing and shipping real work with an agent in the loop, the way Dypsis builds.
What we built
- 01Hands-on sessions built around real agent-driven workflows.
- 02How to spec, prompt, review and verify what an agent produces.
- 03Guardrails so agents raise quality instead of eroding it.
- 04The patterns Dypsis uses to ship production work with agents.
How it works
What it can do
Agent-driven workflow
Plan, build, review and ship with an agent in the loop.
Prompt & verify
Spec well, then check what the agent produced.
Quality guardrails
Keep the speed without eroding the codebase.
Real-world patterns
How we actually ship with agents day to day.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
A practical programme that gets developers genuinely productive with AI coding agents
The developer
Decideseverything the agent proposes. The training exists to make that judgement sharper
Seesthe agent’s output next to the spec it was given, and the checks that verify it
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
Your developers stop fighting the tools and start shipping with an agent in the loop, the same way we build.
Developers who can spec an agent, then review and verify what it produces.
Guardrails in place, so the speed does not cost the codebase.
The same patterns Dypsis uses to ship production work with agents.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Model layer
- AI coding agents
- Runs on
- Claude Code
- Method
- Hands-on labs
Common questions
The same answers as above, written out.
A practical programme that gets developers genuinely productive with AI coding agents, not just autocomplete, but planning, reviewing and shipping real work with an agent in the loop, the way Dypsis builds.
Most developers have tried an AI assistant and bounced off it. Used naively it produces plausible-but-wrong code; used well it changes how a team ships. The gap is skill, not the tool.
Developers work through real tasks with an agent. They learn to spec, review and verify its output. They leave shipping faster, with guardrails in place.
The developer decides everything the agent proposes. The training exists to make that judgement sharper. They see the agent’s output next to the spec it was given, and the checks that verify it.
Model layer: AI coding agents. Runs on: Claude Code. Method: Hands-on labs.
Developers who can spec an agent, then review and verify what it produces. Guardrails in place, so the speed does not cost the codebase. The same patterns Dypsis uses to ship production work with agents.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
- Training
- Developers
- AI agents
- Training / Enablement
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