AGENTIC AI
Anyone can demo an agent.
I run them in production.
Forty vendors will show you an agent booking a meeting. The question that matters is what it does at 3am when the API times out, the data is malformed and nobody is watching. That is the part I build.
WHAT AGENTIC ACTUALLY MEANS HERE
Decides, not executes.
A script
Does exactly what it was told, in order, or dies. When the input shifts, it ships wrong answers quietly.
An agent
Looks at what came back, decides the next step, retries with a different approach, escalates to a human when it should.
The difference
State, tools, evals and cost ceilings - engineering, not prompts. That is what separates a system from a demo.
THE STACK
BUILT FROM SCRATCH
I built my own agent harness. Multiple times.
Once for cloud constraints, once for a local machine - because the two have nothing in common. Queues, retries, token budgets and observability in the cloud; latency, privacy and offline behaviour on the desk. Most people consume a framework. Building the harness twice is how you learn what the frameworks are hiding.
WHAT I BUILD FOR BUSINESSES
Agents with a job.
Ops agents that clear queues. Research and ingestion that reads what your team can't get to. Decisioning with a human in the loop where it belongs. Internal automation tied to a number someone actually reports on - not capabilities, outcomes.
WHEN IT GOES WRONG
Anyone can build an agent.
Few can keep one alive.
The architect section, because this is where the demos die:
Retries & fallbacks
Every tool call fails eventually. The system's answer to that is designed, not discovered.
Evals
A model update can silently change behaviour. Evals catch it before your customers do.
Cost ceilings
An agent in a loop is a credit card in a loop. Budgets are enforced in code, not in hope.
Human in the loop
Some decisions should never be autonomous. Knowing which ones is the job.
I ALSO TEACH IT
learnery.io
AI training your team will actually finish - a five-step, hands-on seminar teaching your people to use AI tools confidently, tailored to your industry and their exact roles. Teaching a subject is the strongest proof of expertise in it, and some teams need the capability in-house, not delivered.
See learnery.ioALREADY SHIPPED
AI in production before 2026.
seethemenu.online has shipped an AI assistant to real businesses since before the pivot year. learnery.io is an AI product live right now. The claim on this page has receipts that predate the hype - URLs, not adjectives.
DATA & GDPR
Where your data goes,
answered before you ask.
EU posture by default: data minimisation, EU-resident processing where the stack allows it, retention that is decided rather than accidental, and a written answer to "what does the model see". I have built GDPR-compliant systems for a decade - the agent work inherits that, it does not escape it.
Have a process that should run itself?
Bring the process and the number it burns. I'll tell you on one call whether an agent is the right answer - including when it isn't.
Start a project