/training
// for engineering teams
Train your team to ship with coding agents.
More productive engineers. Better code, faster. AI agents that are safe to operate, taught by someone who runs them in production every day.
Most teams have coding agents but no discipline around them. So the speed lands in a few hands and the risk lands everywhere else. I close that gap, from teams running hard multi-agent systems to engineers who just want to write better code, faster.
// why now
The honest framing: agents are the next thing platform engineering and SRE are responsible for.
Like any production system, they need context, guardrails, observability, and a way to recover when they go wrong. Run them like production systems and the gains stack. Treat them as magic and you get random output and risk nobody's watching. That's the discipline I teach: the boring, repeatable practice that makes their output safe to merge.
// what your team gets
Speed that survives code review.
Your engineers move from typing code to directing it. They ship more and stop hand-writing the parts an agent does better.
Agents that work inside your tests, reviews, and standards produce reviewable changes. Speed without dropping the quality bar.
Clear rules for what an agent may touch, what needs a human in the loop, and how to stop, roll back, and recover when something looks wrong.
Context, decisions, and patterns from each session get captured and reused, so the team gets faster instead of starting cold every prompt.
// the proof
From my own open-source delivery, measured from git history over a single 20-day build, not a slide.
Your mileage will differ, and one project isn't a guarantee. But good agent operation is something you can measure and verify. The case studies are on /work, and the method is at /method.
// safe operation
Prompting isn't the skill. Operating is. That's what your team walks away knowing how to do:
- What the agent is allowed to change, and what stays off limits across the repo, the pipeline, and production.
- When it can act on its own, when it must propose, and when a human has to decide.
- How to give it the context it needs so its output is right.
- How to capture the work so it compounds instead of disappearing into a chat log.
- How to stop, revert, and recover safely when a change goes sideways.
// how we work
Hands-on, on your real work.
We work in your codebase, on your tasks, side by side. I pair with your engineers, set up the guardrails with you, and teach by doing. That can be a focused workshop, an embedded stint while your team ramps, or ongoing support as the tools change. We scope it to what your team needs.
// who I work with
Hard multi-agent systems at the highest reliability and security bar. I help you operate them like any other critical system: guardrails, observability, and recovery paths that hold up under scrutiny.
You have the tools; you want the discipline that turns them into real throughput without dropping the quality or security bar.
Whether you want serious multi-agent workflows or just better code, faster, we work one-on-one with your real work as the training ground.
// why me
I'm Boden Fuller, a platform engineer with a Master's in Cybersecurity. I spend my days making AI agents productive, reliable, and secure under real production constraints. The tools and method I teach are open, so you can read the code, the case studies, and the principles before we ever talk.
This is my own independent practice and teaching, on my own time and tools, separate from and not affiliated with or representative of any employer.
The first conversation is free. We'll figure out where this pays off for your team, and where it won't.