AI Agent Governance · Control Plane
Deploying an AI agent is easy.Proving what it did is not.
Skillnib is the control plane for organisations putting AI agents into real work. It makes agentic workflows deterministic, manageable and audit-ready by default — with any AI model, in any department.
Governed record — example
Every deliverable Skillnib produces carries one of these. This is the product.
The problem
Agents are already in production. Oversight is not.
Enterprises now have AI agents writing code, drafting contracts and running campaigns. Very few can answer three questions about any single thing an agent produced.
The gap is not capability — agents are demonstrably capable. The gap is control. A prompt is not a process, a chat log is not an audit trail, and a model that behaves well in a demo is not a model that behaves predictably at scale. Under the EU AI Act, "we asked it nicely" is not a compliance posture.
What did it actually do?
Not a transcript — a structured record of the acts performed, in order, with their inputs and outcomes.
Who authorised it?
A named human, holding a real role, approving a specific direction before the work began.
Can you prove it?
To an auditor, a regulator or a customer — months later, without relying on anyone's memory.
What Skillnib is
Humans govern. Agents execute. The engine keeps them honest.
Skillnib is a governance layer that sits between your organisation and the AI doing the work. Agents cannot write directly — every change passes through governed operations that check the rules first. Work follows a defined path with quality gates, and a version increment requires a human signature.
The result is not an AI that is watched. It is an AI that structurally cannot skip a step, bypass a gate, or produce an unattributable result. Determinism by architecture, not by prompt discipline.
Architecture
Five engines, one governed substrate.
Each engine alone resembles a category you already know. No existing product combines all five into a single system where each constrains the others.
Workflow
Sequences any business process through defined steps with quality gates and sign-off. Each vertical defines its own steps; the engine enforces them all.
Versioning
Tracks state through Baseline.Iteration.Patch, where every increment requires human sign-off. It tracks progress toward a promise, not technical change.
Governance
Rules, policies and quality standards as executable constraints an agent must satisfy — validated at every step, with a stated enforcement level.
Skill
Knowledge with hierarchical inheritance: platform to vertical to customer to project. An agent receives exactly the knowledge its task calls for.
Context
Not "what happened last time" — which rules apply, which skills are active, where the workflow stands, and what has already been decided elsewhere.
Runtime
What happens when an agent starts work.
01
Context loads
The agent learns where it is, what exists, and the state of everything it may touch.
02
Skills activate
Only the knowledge this task requires, inherited down to the project level.
03
Governance constrains
Rules bind before the first write. A blocked act is refused, on the record.
04
Workflow sequences
The work advances one gate at a time, and stops where a human must sign.
What it gives you
Four properties, by default.
Deterministic execution
The same work follows the same governed path every time. Steps cannot be skipped and gates cannot be bypassed, whichever model is running.
Audit-ready by default
The audit trail is not a logging feature added afterwards — it is how the system works. Every version traces to the decision and the signature behind it.
Built for the EU AI Act
Human oversight, traceability, record-keeping and risk controls are the architecture rather than a policy document. Enforcement levels are declared per rule: hard_block, warning, advisory.
Your data, your boundary
Context, documents, decisions and audit logs live in the customer's own environment — structured and queryable, never in a third-party memory layer. Change AI providers and the history stays intact.
Model-agnostic
Bring any agent. The governance does not change.
Because context is structured data rather than prompt engineering, and skills are knowledge rather than instructions, any sufficiently capable agent can consume them. Claude, GPT, Gemini — the platform does not care which model runs the task. It cares that the task follows the governed path and meets the defined standard.
Multiple agents run in parallel across departments — one building a feature, one reviewing a contract, one refining brand guidelines — inside the same governed environment, on the same audit trail. No agent can bypass a quality gate, regardless of provider.
Where we are
Pre-seed. Building with early adopters now.
Skillnib is built by a founder-led team and is already governing its own development — the platform runs its own roadmap through its own engine. We are opening a small number of enterprise pilots.
Who we want to meet
- Pre-seed and seed investors
- Early adopter enterprises
- Compliance and AI regulation expertise
- EU AI Act advisory
- Enterprise security expertise
What we offer
- Early-stage investment opportunity
- First-mover position in AI agent governance and control planes
- Enterprise pilot and early adopter access
- A deterministic control architecture and EU AI Act compliance path