Solution · AI Operations
Govern your company's AI agents: the control layer that decides what they can do, keeps a record of everything, and responds when one goes off the rails
You have AI agents scattered across several teams, acting on real systems. The question is no longer whether they work: it's who governs them. What they're allowed to touch, who approves the sensitive stuff, what gets audited, and what happens when one drifts. That isn't a panel you watch; it's a continuous function you run. We build the governance layer and put it to work.
The problem
You have AI agents acting in production. Nobody knows for sure what they can do or who answers if one gets it wrong.
- Each team built its own agent: one writes to the CRM, one sends emails, one touches billing —and nobody has the map of what each one can touch.
- An agent has access to more than it should "because it was easier that way", and nobody has reviewed those permissions since the day it was set up.
- When an agent does something odd, there's no record of what it decided, on what data, or under which model version —so investigating the incident means reconstructing it from memory.
- The EU AI Act lands and you can't classify your agents by risk or show there's human oversight where the law requires it.
Cost of staying the same
A fleet of agents without governance doesn't fail on launch day: it fails the day one acts outside its lane and nobody notices until the damage is done. Permissions nobody reviews, decisions with no trail, incidents put out by firefighting, and an audit —internal or regulatory— you can't pass because there's nothing to show. The cost shows up on no report until it shows up all at once: data that shouldn't have left, an action that shouldn't have run, a fine. Governing is what keeps the first sign of the problem from being the problem.
The solution
We build the governance layer for your fleet of agents —policies, permissions, trail, incidents and compliance— and run it as a continuous function
- 1We inventory which agents you have live, what each one touches and with which permissions: the map that doesn't exist today. From it comes what's over-permissioned, what acts unchecked and what overlaps.
- 2We set the rules of the game: what each agent can and can't do, which actions require human approval before they run, and which cost and scope limits are never crossed. Guardrails, not good intentions.
- 3We keep a record of everything: every action by every agent is logged —what it did, on what data, under which model version— so auditing an incident means opening the log, not reconstructing it from memory.
- 4We build the continuous operation: quality evaluation, incident management with an owner, model version control, and the risk classification the EU AI Act will ask of you. We run it, or we hand it over documented.
What changes
What you stop losing
Agents stop acting on what "someone configured once": each one has permissions scoped to what its function needs, reviewed, not inherited from setup day.
Mechanism
An incident stops being a blind investigation: the audit trail tells you which agent did what, on what data and under which version, so it's fixed in hours instead of reconstructed over days.
Mechanism
Compliance stops being a last-minute scare: agents end up classified by risk and with human oversight documented where the EU AI Act requires it, before an auditor asks.
Mechanism
What we measure: agents with reviewed permissions vs the total, coverage of actions with an audit trail, time to detect and close incidents, and sensitive actions that pass through human approval.
What we measure
Spec sheet
- Work it removes
- having AI agents acting in production with no policies, no reviewed permissions and no record of what each one does
- Typical setup
- 2–4 weeks the setup; continuous governance from the start
- Input
- your AI agents in production and access to their permissions, logs and connected systems
- Output
- the governance layer running: inventory, policies and guardrails, scoped permissions, audit trail, incident management and risk classification
- Works with
- AI agents in productionSSO / IAMLogging stack
- Can connect to
- Your identity and permissions stackYour observability and logging stackThe Implementa AI Operations governance framework
- What we measure
- agents with reviewed permissions vs the totalcoverage of actions with an audit trailtime to detect and close incidentssensitive actions that pass through human approval
- Good fit for
- companies with several AI agents or systems already acting in production that need to govern them as a function —control, trail and compliance— before an incident or an audit forces them to
- Not a fit for
- anyone who doesn't have an agent in production yet and is looking to build the first one; to govern you need something to govern
Frequently asked questions
No. A dashboard shows you what happened; governance decides what can happen. Here you set each agent's permissions, the actions that require human approval, the cost limits and the audit trail —and someone runs the incidents when something goes off the rails. Watching isn't governing.
AI Operations as a service runs your systems: it watches that they keep working and iterates. This is the control layer above: what agents are allowed to do, who approves it, what gets audited and how you meet the EU AI Act. You can have one without the other, but a fleet that grows without governance is exactly what ends in an incident.
If your agents make or assist decisions about people —customers, candidates, employees—, yes. The regulation classifies systems by risk and requires human oversight and traceability for high-risk ones. We classify your fleet and leave that oversight and trail in place before an auditor asks, not after.
Want it running in your business?
You’ve pinned the problem. We ship the fix and leave it measured.