One job, defined tightly
Narrow enough that we can describe how to tell whether it worked, and test it against that.
Testable instead of hopeful
Service
AI agent development for work that has more than one step
We build narrow agents that do a specific job inside your systems: gather what is needed, take the action, and stop and ask when something falls outside what they were built for.
In short
An AI agent is a system that can carry out a multi-step task rather than answering a single question. It works out what needs doing, gathers the information, takes the action, and checks the result. The ones that survive in production are narrow and specific, with clear limits on what they may do and a person on the other side of that limit. General-purpose agents demonstrate well and break on the second step.
Works with the systems you already run
Claude
OpenAI
n8n
Slack
Jira
AirtableThe problem
The demo always works. The second week is the test
An agent handling the expected case is straightforward and looks impressive. Then it meets a supplier who has changed their name, a form with a field missing, a system that times out. A general-purpose agent will improvise, and an improvising system inside your business records is not a feature.
The ones that last are boring by comparison. They do one job. They have a written list of what they may touch. When something does not fit, they stop and hand it to a person with an explanation rather than pressing on.
It breaks in four places, and the people doing it feel every one.
- 01Too broad to trust
An agent built to handle anything handles nothing reliably
The wider the remit, the more ways it can be wrong, and the harder it is to say whether it is working. Narrow agents can be tested. General ones can only be hoped for.
- 02No limits
Nobody wrote down what it is not allowed to do
If the boundary is not explicit and enforced, it is a matter of luck. That is fine in a demo and unacceptable anywhere near money, customers or records.
- 03Silent improvisation
It makes something up rather than stopping
The failure that damages trust permanently. One invented reference number in a real record and nobody in the business will believe the system again, correctly.
- 04No trail
You cannot see what it did or why
When something goes wrong, the first question is what happened. Without a step-by-step record there is no answer, so there is no fix, so the whole thing gets switched off.
What changes
The same week, run differently
How it runs nowHow it runs after
An agent that will attempt anything, unpredictably.
An agent that does one job and is tested on it.
The boundary is a hope.
The boundary is written down and enforced in the system.
It improvises when reality does not match.
It stops, explains, and hands to a person.
Nobody can explain what it did.
Every step is logged and can be replayed.
What we build
How we build agents that survive contact with production
Limits built in, not requested
Which systems it can reach and what it can do, enforced by the system rather than written in an instruction.
The boundary actually holds
Stopping behaviour first
What happens when information is missing or the situation is unfamiliar, built before the main path.
It hands over instead of inventing
A full record of every run
Each step it took and why, so a problem can be found and fixed rather than guessed at.
Fixable, so it stays switched on
Proof
We build these as small specialists that hand work to each other rather than as one agent that tries to do everything. In a research system that means one part gathering, another checking the numbers, another preparing the summary, each with its own limits. It is less impressive to describe and considerably more likely to still be running in six months.
How it works
From first call to running system
- 01
We pick a job narrow enough to test
One task, with a definition of done you could check by hand. If we cannot describe how to tell whether it worked, it is not ready to be built.
- 02
We write down what it may and may not touch
Which systems, which actions, up to what value. This list is the safety mechanism and it belongs to you, not to us.
- 03
We build the stopping behaviour first
What happens when the information is missing, the system is down, or the situation is unfamiliar. Getting this right before the happy path is what separates a production agent from a demo.
- 04
We run it visible before we run it wide
Live on a small slice with everything logged and a person watching, then widened once you can see how it behaves on real work.
Confidence & control
What happens when the system is unsure
- It stops rather than improvising
- When the situation does not match what it was built for, it hands over with an explanation. A system that guesses to avoid admitting uncertainty is the single fastest way to lose a team's trust in it.
- The limits are enforced, not requested
- What it may touch and up to what value is built into the system rather than written in an instruction it might ignore. Anything outside that stops and waits for a person.
- Everything is logged and replayable
- You can see each step it took and why. When something goes wrong that record is the difference between fixing it and switching the whole thing off.
- You own it when we leave
- It is built inside your own accounts and your own cloud. If you never speak to us again it keeps running, and another team could pick it up. You are not renting your own process back from us.
Scope
What an engagement covers
Included
- A written definition of the one job and how to tell it worked
- An explicit list of what the agent may and may not touch
- The stopping and escalation behaviour, built before the main path
- Connections to the systems the agent needs
- A full step-by-step log of every run
- A supervised period on live work before the scope widens
Not included
- A general-purpose assistant. We build narrow agents because those are the ones that keep working.
- Agents that act on money, contracts or customer records without an explicit approval step.
- Training a model on your data. That is a different engagement with different economics.
- A promise that an agent is the right answer. Often a simpler automation is, and we will say so.
Scope, timeline and price are agreed after a short call, never before. We write down what the system has to do, and what it costs, before any build starts.
Questions
Frequently asked
What is the difference between an agent and an automation?
An automation follows steps you defined. An agent works out the steps within limits you set. Agents are worth the extra complexity when the path genuinely varies each time. When it does not, a plain automation is cheaper, faster and more reliable, and we will tell you which you need.
How do you stop it doing something it should not?
The limits are enforced in the system rather than written as an instruction. It can only reach the systems we connected and only take the actions on the list, and anything above an agreed value stops for a person.
What happens when it does not know what to do?
It stops and hands over with an explanation of what it was doing and where it got stuck. Building that behaviour first, before the main path, is most of what makes an agent safe to run.
Can we start small?
You should. One narrow job, live on a slice of real work with someone watching, is the only way to find out how it behaves on your data. Widening after that is straightforward; starting wide rarely recovers.
Do we need our own model?
Almost never. For the work most businesses want done, the available models are already capable and the difficulty is in the connections, the limits and the failure handling.
What does it cost?
Every engagement is priced to its own scope, so there is no list price. After a short discovery call we agree in writing what the system has to do and what it costs, before any build starts.
Related
Other things we build
Bring us the workflow that keeps eating your team's week.
Let's find the first one to fix.
The audit is free. If we can't find automation worth more than it costs to build, you owe us nothing, and you keep the roadmap.
Prefer email? info@chronexa.io
Or tell us what's slow
We'll review your workflows and come back with where AI saves the most time and cost.