Put AI agents into production, with control built in
Getting an AI agent to work in a demo is easy. Getting it approved to touch real systems, real customers and real money is where many initiatives stall, because nobody can say exactly what it will do when something goes wrong.
Nucleoo designs and builds AI workflows with the control layer included: clear limits, human escalation and a record of every decision. That’s what lets operations, IT and compliance say yes.
Why AI pilots stall
The questions nobody can answer yet
As soon as an agent can access systems, make decisions or take actions, these questions come up, from operations, IT, legal or the board. Without clear answers, the project either doesn’t go live or goes live as a risk.
- 01What is the agent allowed to do?
- 02What happens when its output is wrong or uncertain?
- 03When should a person step in?
- 04Could you explain it to an auditor or a regulator?
- 05Who remains responsible for the process?
Our approach
Agents with clear limits
We don’t just build the agent. We build the layer around it that decides what it may do, checks what it does and records why, sitting between the model and your systems.
- Language models
- Tools
- Retrieval
Designed and built by Nucleoo
The control layer
- Clear ownershipEvery process has a person accountable for its outcome.
- A full recordEvery decision is logged and can be audited later.
- Explicit limitsAgents only act within the rules and permissions you set.
- People in the loopCritical decisions go to a person, with the context to decide.
- ERP & CRM
- Email & WhatsApp
- Documents
- Teams
Designing how decisions are made comes first. Automating them comes second.
Designing for uncertainty
AI is probabilistic. Your operations can’t be.
For every process we agree up front what the agent may decide on its own, what needs a person and what is off limits.
- Permissions
- Business rules
- Confidence
- Impact
- Within boundariesExecutes automaticallyRoutine, low-risk work runs end to end, at machine speed.
- Uncertain or high impactEscalates to a personThe right owner gets the case with full context, and decides.
- Out of boundsBlocked and flaggedAnything outside the agent’s mandate never reaches your systems.
Every step is logged, observable and evaluated.
Reusable foundations
We don’t start from zero
Every organisation’s processes, systems and risks are different, so this can’t be an off-the-shelf product. But the same problems come up in every agent project, so we’ve built reusable components for them.
- Agent orchestrationCoordinates agents, tools and steps across a workflow, including hand-offs and retries.
- PermissionsScopes what each agent can read, write and trigger in your systems.
- ValidationChecks outputs against rules, schemas and business constraints before they take effect.
- Human approvalRoutes high-impact or uncertain decisions to the right person, with the context to decide.
- ObservabilityTraces every run: inputs, decisions, cost and latency.
- System integrationPatterns for connecting email, messaging, ticketing, ERPs and document stores.
We use them where they fit and design the rest around your process: a custom solution, without paying to rebuild the same infrastructure every time.
It’s not about automating more. It’s about moving faster without losing control.
How we work
Start with one real process
We don’t transform your entire organisation overnight. We start with one process where there is friction today, prove it works, and grow from there.
Where we usually start
- Communication channels (email, WhatsApp, tickets)
- Retrieval from large document collections
- Customer service and back-office platforms
- AI on top of your existing systems
From one process to scale
- 1Understand the processMap the workflow, exceptions, systems, decisions and ownership.
- 2Decide where AI actually helpsNot every step needs an LLM. Some are better handled by software, rules or people.
- 3Design the control modelDefine permissions, validation, escalation, observability and failure behaviour.
- 4Build with proven componentsUse Nucleoo’s reusable orchestration, integration and governance components where they fit.
- 5Measure before scalingEvaluate reliability, cost, latency, exceptions and business impact before adding autonomy or scope.
2weeks
How to start
One process, assessed with your team
The first three steps above. We work alongside your team, with the people who run the process day to day and not only those who sign it off, to understand how it really works and whether automating it with AI is worth it.
It usually takes two weeks. If a process is too big for that, we agree up front which part to start with, rather than stretching the timeline.
No theory and no generic AI roadmap: a clear, realistic answer for one process, including when the answer is “not yet”.
From your side: time with the people who run the process and access to the systems involved.
What you get
- A map of the process as it really runsSteps, systems, decisions, exceptions and owners, built with the people who do the work.
- Where AI helps, and where it doesn’tStep by step: what an agent should handle, what is better as rules or software and what stays with people, with the value of each.
- A control modelPermissions, validations, escalation points and what gets recorded.
- An estimate you can plan withA range for scope, timeline and cost to build it, and what to measure before scaling.
What you can show an auditor
The EU AI Act is already in force, and its obligations are being phased in. Whatever applies to your case, you’ll need to show how your AI systems are controlled.
Because control is part of the design, the evidence comes with the system:
- A permission map: what each agent can read, write and trigger.
- A decision log: the inputs, outputs and actions behind every outcome.
- Escalation rules: when a person steps in, who it is and what they decided.
AI running in production
