What agentic-dev training actually looks like

Short version: most "AI training" is a webinar, a prompt cheatsheet, and a sense that your team should be doing more. That doesn't move a team from copilot to operator. Real agentic-dev training is hands-on, on your own codebase, with your devs running agents on real work while someone who does this in production sits beside them. It ends with the capability living in your team, not in a slide deck.

Why the usual "AI training" doesn't stick

A deck explains what agents can do. It can't give your devs the judgment for when to trust an agent, how to scope work for one, how to review its output at the right altitude, and when to step in. That judgment only forms by doing the work on real code, with feedback. Generic training skips the only part that matters.

It also tends to land as "here's a tool, good luck," which is exactly how AI rollouts stall: the devs who suspect it's there to replace them quietly route around it, and the rest use it as fancier autocomplete.

What we actually do

Training is an engagement on your repo, not a course. Roughly:

1. Start on real work. We pick actual tickets from your backlog and run agents on them with your devs, in your environment. No toy examples.
2. Build the judgment. How to scope a task for an agent, set guardrails, read the diff and the test output critically, and recognize the failure modes (confident-wrong, scope creep, stale context) before they cost you.
3. Set up the supporting layer. The context, orchestration, and observability that make agents reliable enough to trust on your codebase, so the training has something solid to run on.
4. Upskill the leads. Your senior devs and CTO learn to operate and to coach the rest, so the practice spreads after we leave.

The shape varies by team. With a quant-finance team (Clareo Systems), it meant agentic-dev training plus standing up the process and guardrails their controls demanded, and upskilling the CTO to lead it. The constant is: real work, your standards, your people operating.

Augment, never replace

The framing is load-bearing, not a nicety. Training works only when devs believe the agents make them stronger, not redundant. So the whole engagement is built around your developers becoming the operators. The craft stays theirs; the output gets bigger. A team that trusts the tool pushes it to its limit; a team that fears it won't.

What you have when we leave

Not a certificate. A team that operates agents on production work, the supporting layer running on your repo, and leads who can coach the next person. The goal is independence: if you still need us in six months to keep the agents running, the training failed.

How tsukumo does it

We run agent fleets in production to ship our own software, so we teach from practice, not theory. We bring the operating model we run ourselves onto your codebase and your standards, and train your devs to own it.

If your team has the seats and you want them operating at the level seats alone won't reach, that's the work. Talk to us about your team.