Agent Governance & Guardrails
So the agent may do exactly what it’s allowed to. And you can prove it.
Governance isn’t a document. It’s code.
An AI agent gets access to systems, data and tools. It makes decisions that have consequences.
If no one limits what it may do, and no one logs what it has done, no compliance officer will let it into production. Rightly so.
So we don’t write governance into a policy. We write it into the system. Permissions that are enforced. Approvals that can’t be bypassed. Logs that are complete.
What we do and what we don’t
We deliver the technical evidence your compliance department needs: audit logs, permission matrices, decision records, test evidence. The legal assessment is made by your legal department, not us. We are engineers.
Every agent action passes through the same controls
Not in a policy someone can bypass. In the system, which otherwise wouldn’t let the call through in the first place.
If one of the controls fails, the call is rejected and the attempt is logged. No silent bypass.
What we offer
Six controls that turn an agent into a system fit for approval
Permission model
Role-based access for agents. Which agent reaches which system with which permissions.
Tool access & sandboxing
Every tool released individually, every call validated, every failed attempt logged.
Guardrails & policy enforcement
Rules enforced in the system, not written in a policy. Input and output filters, topic and action limits.
Audit logging
Gap-free logging of every agent decision: input, context, tools used, output, timestamp, model version.
Approval workflows
Defined thresholds above which a human must confirm. With cover arrangements and evidence.
Cost and usage control
Token budgets per agent, per department, per period. With alert and hard limit.
Project example
From open question to provable approval
Agent governance in a regulated authority
A public institution wanted to deploy an assistant agent that gives staff information from internal rulebooks. Approval initially failed because no one could prove which documents the agent had accessed in a given case. We implemented a permission model that restricts access per user role, and added a logging layer that immutably records the input, the sources drawn on, the model version and a timestamp for every answer. Every answer includes visible source references. Requests about personal cases are refused and the attempt is logged. The data protection department granted approval after a three-week review.
Every agent decision logged without gaps
Approval obtained from the data protection department
No access outside the role model
Seven more building blocks to a production system
AI Readiness Assessment
Inventory, vulnerability analysis and roadmap to production. In 10 working days.
Learn morePrivate AI & On-Premises
Run AI inside your own building: local models, your own hardware, full data sovereignty.
Learn moreAI Security & Compliance
Hardening, ISO-oriented evidence and data protection for the AI operation.
Learn moreProduction Hardening
Make the existing pilot fit for operation: error handling, edge cases, human-in-the-loop.
Learn moreAI Infrastructure & Deployment
Cloud, data pipelines, version control and deployment with rollback.
Learn moreEnterprise System Integration
Connect agents to ERP, CRM and legacy. With permission model and rollback.
Learn moreEvaluation & Managed Operations
Quality checks, monitoring, early warning of quality decay and incident response in day-to-day operation.
Learn moreRetrofit governance?
We check which evidence your approval is missing today, and deliver it as code.