I find where agents and automation create leverage across your business: sales, operations, engineering, your data and knowledge layer. Then I build it, and train your team to run it. Governed and safe from day one.
Why agents
Coding agents automate tasks that were never possible to automate before. They multiply what your employees can do, stand up an enterprise-level data layer, and build internal tools that actually connect and scale every part of the business. I find where that leverage lives and build it in.
Why now
Most enterprises already have the licenses and a few pilots. Few have agents built into how the business actually runs, and that gap compounds.
Competitors who close it ship in days what used to take months. The move is to find where agents create leverage and build it now, while the advantage is still open.
What I install
Coding agents aren’t just for engineers. Used well, they become an operational layer across the whole company: sales, internal tools, data, knowledge, engineering. I find where they create value and build it in, and teaching your team to wield it is part of that same build.
Most company data isn’t in a form agents can work with. I make it accessible, with the right structure and the right access patterns, so your agents operate on real business context instead of guesses.
Everyone picks their own tools and nothing connects. I standardize the suite, what to deploy and what to drop, so the whole company runs on one coherent setup.
A structured, navigable layer your agents pull from only when they need it, the way an engineer navigates a codebase, instead of a slow central server that bloats every request.
Custom agents and pipelines that run real workflows end to end, not demos. The layer where a smaller team starts producing the output of a much larger one.
Deploying agents across a regulated enterprise means compliance, security, and evaluation have to be in the architecture from the start. I build them in from day one instead of bolting them on later.
The team capability to operate all of the above. My MACHINE curriculum, taught from first principles and rolled out across departments, so coding agents land in every team and not just the dev org.
How I engage
One engagement arc, scoped to your business. It starts small and paid, so you get something concrete before any larger commitment.
A focused, paid engagement where I study your stack, data, and processes, then map exactly where coding agents and automation create leverage and how I’d build it. You walk away with a concrete plan whether or not we go further.
I architect and ship the systems: agents, automations, and the data and knowledge layer they run on, built into how your business actually works. These run in production.
When it helps, I train your team to operate and extend it through the MACHINE curriculum, so the capability stays in-house after I’m gone.
The curriculum
When enabling your team is part of the answer, this is the framework I teach it through. AI has real depth as a skill, and people underrate it because the interface looks approachable, then pay the technical debt later. MACHINE is seven pillars, taught from first principles, provider-agnostic for when the tools change.
How to plan and scope work with agents.
How to create AI agents and orchestrate them to do work.
How to optimize what is in the model’s active memory.
How to understand Claude Code and build robust automations.
Knowing how AI works and the failure patterns, so you can prevent them before they happen.
How to prompt engineer effectively.
How traditional engineering mixes in with AI.
Proof
Engagement outcomes
Way2b1 · Fintech software startup
Trained the DevOps department. Within the first month, the engagement produced a normalized onboarding and understanding of Claude Code, a standardized LLM-friendly codebase, a 2x increase in productivity with agents, and fully automated ticket-to-PR pipelines in production.
Meistro.ai · John Stern, co-founder & CIO
Shipped a production synthetic fault-detection system during our engagement. 30-year CIO, formerly at Take-Two Interactive (1,200-developer org), Deloitte, and Google Cloud Healthcare AI; Fortium partner. Available as a named reference on request.
Direct testimonial
“Our team was absorbed in a merger. Everyone left and I was the only one still there, training their replacements. AI was the only way I could carry the team’s workload while the new hires came up to speed. When I started working with Roman, my output went roughly 5x. I ran my first internal AI workshop using his frameworks, and 50 people showed up. Now I’m building a fully automated PR-review pipeline so I can stop spending my day monitoring my team’s output.”
Who
I’m Roman Colman. I worked at Los Alamos National Laboratory in AI research. I have three published papers, including an IEEE publication and a NeurIPS spotlight, which is a top 3% paper at the world’s premier AI conference. I’ve put over three thousand hours into Claude Code, and I turned that obsession into The Agentic Lab: a community and movement with thousands of members.
An engagement scales beyond me. Through The Agentic Lab I can draw on 2,200 vetted technicals on demand, many trained directly on how I think about building with AI, so the work scales without the risk.
I spend much of my time scaling my business using Claude Code for development of internal tools and automations, marketing, outreach, and research. The other large portion of my time is spent helping The Trajectory Engineers, a cohort of real business owners and software developers building real businesses and software with coding agents.
If you’re a CEO, CIO, or fractional CIO who knows AI should be doing more but not what or how, let’s talk. One call, peer to peer, and we scope an assessment from there.
