AI engineering training

Stop tokenmaxxing. Put engineers back in control.

Two hands-on formats for software teams that need better context-window discipline, fewer blind agent loops, lower token waste, and stronger engineering review.

Book a 30-min intake Compare the formats

The problem

Your engineers think they control AI. Your token bill says otherwise.

Tooling without training

Most engineers learned agents on the job. They know the interface, not the operating model behind the harness.

Context without discipline

Oversized prompts, vague tasks, and polluted context windows make the model expensive before they make it useful.

Usage without accountability

If nobody can explain when to stop the assistant, the cost curve becomes the first visible signal of lost control.

Who decides

AI governance is now an executive responsibility.

Governance goals

Tech C-level

Make AI a disciplined engineering tool again.

Protect quality, tests, code review, architecture, CI/CD, and deployment workflows
Move engineers from AI validators back to owners of the SDLC
Rebuild team cohesion, knowledge sharing, engineering craft, and talent retention

Other C-level

Govern AI adoption without paying the hidden cost.

Turn AI productivity into governed leverage, not uncontrolled token spend
Reduce strategic, security, financial, and organizational risk
Avoid technical debt from poorly piloted AI transformation programs

Formats

Choose between deep practice and fast team alignment.

Custom intra-company workshop

Controlled Agentic Engineering

A hands-on training path for cohorts of 10 to 15 software engineers, adapted to your use cases, stack, codebase reality, and delivery rituals.

Format: 1 day split into two 4-hour half-days, separated by a few weeks
Mode: Show, explain, make engineers do, then debrief on real workflows

Master context windows, context packs, and task boundaries
Understand harnesses, agents, skills, prompts, and thinking-token tradeoffs
Practice planning, validation, tests, review, and controlled delegation

Scope the workshop

Internal masterclass / meetup

Tokenmaxxing Rehab

A 3-hour on-site session to create a shared wake-up call, align engineers on good AI usage, and introduce controlled context discipline.

Setup: Manuel remote from the US for demos; Pierre on site to run the room
Mode: Dense demos, practical exercises, pair work, questions, and time-boxed discussion

Expose tokenmaxxing, context dumping, and fake AI velocity
Give engineers a shared language for better agent usage
Create momentum before a deeper workshop or internal rollout

Discuss the masterclass

Method

Bring software engineering fundamentals back into the AI loop.

Diagnose current AI usage

Identify where assistants help, where they hide rework, and where teams have no shared operating rules.

Control the context and the harness

Teach how context windows, prompts, skills, tools, agents, and thinking tokens actually shape output quality.

Practice deliberate AI-assisted engineering

Engineers work on realistic flows: debug, refactor, feature work, tests, review, validation, and stopping criteria.

Codify team protocols

The team leaves with habits managers can inspect and engineers can apply after the session.

Signals

Built from real software delivery work, not generic prompt training.

Next step

Start with a 30-minute intake.

We qualify team size, current AI tools, maturity, delivery pain, decision makers, and whether the right entry point is a workshop or a masterclass.

Bring five facts: team size, AI tools in use, main delivery pain, target date, and who sponsors the initiative.

Request the intake