Evaluation
An agent is tested against your real cases before it touches real work, and re-tested when the model, the tools, or the job change. If you cannot measure it, you cannot trust it.
Consulting · Colorado
Agentic AI Consulting in Colorado
I help Colorado teams put AI agents to work: not demos, but systems their people use every day, that survive contact with real life. An agent takes a task, uses your tools, checks its work, and hands back a result, with review steps where a human stays in charge.
What an agent is, and is not
A chatbot answers a question and forgets it. An agent carries a task through: it reads from your systems, acts in them, and reports what happened. That is why the controls matter more than the model. The work I do is the whole system around the agent, not just the prompt that starts it.
I build on my own agent systems, Operario and AI Maestro, and on the stacks your team already runs. Operario took 3rd of 25 at the Frontier evaluation, and both systems exist because I needed agents that hold up inside real operations, not just in a demo.
AI Maestro, built on the open protocols AMP, AID, AAP, and AFP; ChatGPT hackathon winner, January 2024 (Team Triumph); Operario 3rd of 25 at Frontier (honest results).
What production agents need
Permissions and provenance
The agent acts with the access it actually needs, nothing more, and every action leaves a record of what it did and why. Audit trails are part of the system, not a report added later.
Observability
You can see what the agent is doing while it runs: which tools it called, what it found, and where it got stuck. A system you cannot watch is a system you cannot run.
Human review where it matters
The steps with real consequences stop for a person. The agent drafts, gathers, and prepares; a human approves. That is the review step, and it stays in the workflow.
Cost and limits
An agent that works is an agent you can afford to run. Cost per task, latency, and failure modes are measured from the first pilot, not discovered in the invoice.
How an engagement works
We start with one workflow: the job that repeats, costs the most time, and has clear review points. The agent is built inside your real operation, tested against your real cases, and reviewed with your team at every step. When it earns trust, it expands to the next workflow. When it does not, we fix the system, not the demo.
Based in Boulder County, working with teams in Boulder, Denver, and across the Front Range. See also AI strategy, architecture review, and AI readiness assessment.
Questions, answered
How is an agent different from a chatbot?
A chatbot answers. An agent does: it takes a task, uses your tools, checks its work, and hands back a result, with a human reviewing the steps that matter. The difference is not the model, it is the job and the controls around it.
What do we build first?
One workflow, chosen together: the job that repeats, costs the most time, and has clear review points. We build the agent inside your real operation, review the steps together, and expand only what earns it.
Do you work with our existing tools?
Yes. Agents are wired into the tools your team already runs, and built on my own agent systems, Operario and AI Maestro, where they fit. The goal is software your team can run, not a demo that impresses once.
Do you work outside Colorado?
I am based in Boulder County and work with teams in Boulder, Denver, and across the Front Range, and remotely beyond that. The engagement starts with a conversation about your workflow, wherever you are.
Ready for an agent that does the job?
Get in touch
Tell me the workflow that eats your team's time, and we will scope the first agent from there. I read every message myself and reply within two business days.