Put AI agents into production without giving up control.
Governance begins before go-live. We define where the agent operates, what it may access and do, where a person must approve, what happens when confidence is low, and how the workflow can be escalated or suspended.
Your systems. Your credentials. Your agent.
Only what it needs, nothing more. We hold none of your data.
The agent runs in your environment, on your model account, under your data processing agreement. We never hold the content, and you can check the model vendor directly, the same way you check any other supplier.
That answer is the whole security question in four sentences. The rest of this page is the detail behind it.
Customer-side production. Clear platform boundaries.
Two zones and one control layer that spans both.
Your environment
Where the production agent actually runs.
- The production AI agent
- Approved customer systems and data
- User interactions
- Operational actions
The RELIABL.IT platform, EU hosted
What we hold, which is the planning layer and not your operational data.
- The strategy and business information the transformation process needs
- Stored securely and encrypted
- Planning and orchestration layer
The control layer, across both zones
Human control is designed into the workflow.
Before production launch, the selected workflow defines all eight of these. In writing, before anything is configured.
- A named business owner
- The agent's approved scope
- The data and system access it requires
- The actions the agent may take
- The human approval points
- The escalation conditions
- The suspension procedures
- The monitoring requirements
If the agent reaches a condition outside the agreed scope, the workflow stops, escalates, or asks for a person. It does not improvise.
Where a person stays in the loop.
One workflow, eight steps, and who owns each one.
- 01 PeopleThe business owner defines the priority and the scope.
- 02 AI agentThe agent gathers approved context and prepares the work.
- 03 AI agentThe agent drafts the outcome inside its guardrails.
- 04 People ApproveA human approves before the action takes effect.
- 05 AI agentThe agent executes the approved action and records it.
- 06 People EscalateAn exception or low confidence is escalated to a person.
- 07 People StopAn out-of-scope condition stops or suspends the workflow.
- 08 PeopleThe owner reviews monitoring and the agreed measure.
Illustrative structure of lanes and control points. The exact steps, approval points and escalation rules are defined per workflow before go-live.
Human in the loop means specified actions or decisions require human review or approval, and that anything uncertain or out of scope escalates to a person rather than being completed autonomously. The approval model can change only when you deliberately change it.
How the agent reaches your files.
Usually this is asked as a practical question rather than a security one, and the honest answer is small. The owner syncs the document library to their machine the way they already do, drops the documents this one workflow needs into a folder, and the agent reads that folder.
Nothing gets installed on your servers. If you later want a direct connection, it is a scoped least-privilege account for named systems only, never tenant-wide.
If your documents live inside a controlled library under a quality system, we do not touch it. You copy the documents this one workflow needs into a separate working folder outside the quality system, and the agent reads that. The sprint is then not a change to your quality system, which is the difference between 30 days and a validation exercise.
Designed for a European business environment.
The approach is designed to support GDPR compliance and EU AI Act readiness, with EU hosting for the data the platform stores.
The specific legal, regulatory, security and industry obligations still depend on you, the use case, the data, the deployment context and applicable law. We are not going to claim otherwise on a website.
Methodology → What makes an AI agent governed → The platform →
Security and governance questions.
Where does our data actually go?
It runs in your environment, on your model account, under your data processing agreement. We never hold the content. You can check the model vendor directly, the same way you check any other supplier.
The platform holds the strategy and business information the transformation process needs, encrypted, in EU-hosted environments. Not your operational data.
Where does the production agent run?
In your environment, on your credentials, with least-privilege access scoped to the one workflow.
Your systems. Your credentials. Your agent. Only what it needs, nothing more.
Can the agent take real actions?
Yes, within the actions you approved during the redesign, and with a person approving every real action on the early runs.
The gates loosen only when you decide you are comfortable, not before.
What happens if the agent is uncertain?
It stops and asks. Escalation conditions and suspension procedures are agreed before go-live rather than added after something goes wrong.
It runs in a sandbox before it runs for real, and a person checks the output before anything reaches a customer.
Do you guarantee GDPR or EU AI Act compliance?
No, and be careful with anyone who does. Compliance depends on your data, your use case, your deployment context and applicable law.
What we can say is what we designed for: EU hosting for platform-stored data, customer-side execution, least privilege, explicit approval points, and no operational data held by us.
Our documents are controlled. Does this touch the quality system?
No. We do not touch the controlled library.
You copy the documents this one workflow needs into a separate working folder outside the quality system, and the agent reads that.
Find where AI can create measurable value first.
Next day you get a one-page map of where an agent pays first, the metric we'd aim to move, and a fixed price and date.
45 minutes. You keep the map either way.
No preparation required. No commitment.