An AI agent development company that builds the stop button before the second agent
Digiton is an AI agent development company for agents that act inside your own systems: reading records, drafting replies, updating a CRM, checking a document against rules.
AI agent development
- Built first
- The controls
- Stop
- One kill switch
- Limits
- Allowlists and daily caps
- Output
- Verified against sources
- Reference
- Our own fleet, public code
The short answer
An AI agent development company builds software that decides the next step, calls your tools to take it and records what it did. Digiton builds the controls first: one kill switch, recipient allowlists, daily caps, duplicate checks, logs a person can read and a gate that verifies output against sources. Our own fleet runs on exactly that pattern, and the code is public.
What page one sells you
We pulled the UK results for this phrase on 3 October 2026. Three of the ten were listicles ranking agent builders, one was the Clutch directory, three were large or offshore development shops, and one was a software vendor's guide to agent development companies. Two were smaller firms' service pages.
Before you shortlist from those pages, ask each firm how you stop an agent once it's running and who owns it after launch. Then ask how the monthly model bill is kept from creeping up. Those answers decide whether an agent survives its first quarter.
The controls we build before anything acts on its own
Digiton runs its own sales and operations on a fleet of ten scheduled agents. The repository is public at github.com/Botfather90/digiton-agent-fleet, one Node file per agent, and every outbound action passes the same unit-tested rails:
- Kill switch. One file on disk. If it exists, every agent stops taking actions at once.
- Allowlist and do-not-contact list. An agent can only reach addresses it was cleared for.
- Dedupe. The same message can't go to the same person twice.
- Daily cap. A hard ceiling on actions per day, so a bad loop costs a morning, never a month.
Client agents get the same rails, sized to their risk. The write-up on how the fleet is scheduled and logged explains the pattern.
Verification before a user sees the answer
An agent that sounds sure and is wrong does more damage than one that says it doesn't know.
In Parci, our property platform covering 300 of 308 Portuguese municipalities, a dedicated verification agent checks every claim in a draft against the documents it cites, and it can send the draft back. That gate stayed in when we cut the run time to about 47 seconds. The six-agent graph deep dive covers the trade-offs.
Which jobs should be agents, and which should stay scripts
The filter we use on our own fleet is plain:
- An agent fits a job that repeats, has a clear definition of done and carries little ambiguity: watching for a signal, classifying an inbound message, drafting something a person will review.
- A script fits a job that follows fixed rules every time. It's cheaper and safer as ordinary code.
- A person keeps the judgment call on a case nobody has seen before, until the agent has handled enough similar cases to earn trust.
That's why a scoping call can end with us recommending fewer agents than you planned. Agents inside a bigger platform belong under custom AI development, and agents your team already built with Claude under production engineering. Country pages carry local detail: AI agent development in Luxembourg, the Luxembourg AI consultancy page, AI agent development in Ireland and AI consultancy in Dublin.
Digiton Dynamics OÜ is registered in Tallinn, Estonia, and delivers from Lisbon.
Frequently asked questions
What's the difference between an AI agent and an automation?
An automation follows a fixed path you drew in advance. An agent reads the situation, picks the next step and calls a tool to take it. That freedom is useful on messy inputs like emails and documents, and it's the reason an agent needs a kill switch and logs that an automation can live without.
How do you stop an agent that starts doing the wrong thing?
Every agent we build respects one kill switch that halts all actions at once, and a daily cap that limits damage before anyone notices. Logs show what each agent did and why, so the fix comes from evidence instead of guesswork.
Do your agents send emails or messages on their own?
Only if you decide they should, inside an allowlist and a daily cap. Many clients start with agents that draft and a person who sends. Our own fleet does a lot of its work as drafts that a person reviews before anything goes out.
How does an agent engagement run?
It starts with a 25-minute scoping call that costs nothing. You describe the workflow and the systems the agent would touch, and we say whether an agent is the right tool at all. The first paid step is a production review: five working days inside your repository and infrastructure, scored against our ten-layer standard, with a load test and a fix list priced line by line. The build has a fixed scope and ends when agreed acceptance tests pass. After that we can operate the agents, with monitoring and on-call.
Which models do the agents run on?
Mostly Claude. We match the model to the step, so a small model classifies and Claude Sonnet handles most reasoning and tool use, with the largest models kept for architecture-level work. Scheduled agents run on a fixed cadence with bounded jobs, so the weekly cost is known in advance instead of discovered after a spike. The choice is written down per agent so the next engineer understands it.
Who owns an agent after it goes live?
Each agent has a named owner on your side and a handover document written for that person. The code sits in your repository. If we operate it for you, that's a separate agreement from the build.
Talk to an engineer about your system
Book a free 25-minute scoping call. We tell you what we would build, what it takes to keep it running, and whether we are the right fit.