Agent Control Plane
Govern AI workflows at every boundary.
Why control matters
Enterprise AI cannot scale on trust alone.
Production workflows may touch sensitive data, call business systems, create artifacts, use multiple tools, retrieve context across domains, and make decisions that need to be reviewed later. If permissions, policies, lineage, and evaluation are added separately for every workflow, governance becomes inconsistent and hard to prove.
The Control Plane gives enterprises a shared way to enforce rules across AI-enabled work.
It helps ensure that approved access, downstream restrictions, evaluation gates, audit records, and lineage are part of the system of execution — not a separate process bolted on after the work is done.
What the Control Plane does
Enforces who can see what
The Control Plane supports attribute-based access control so context, data, documents, tools, and artifacts can be scoped by identity, role, domain, namespace, classification, and policy.
Controls what can happen next
Once a workflow has accessed governed information, downstream behavior can be restricted. Sensitive context can affect which tools are available, where outputs can go, and which actions are allowed.
Applies manifest-based limits
Workflow manifests define the operating envelope for a given profile, including model policy, tool access, permissions, context scope, and classification ceilings.
Carries lineage through the workflow
Data, tool calls, artifacts, decisions, and outputs can retain lineage as they move through execution, memory, ontology, and Data Room surfaces.
Records decisions for audit
Admissions, denials, dispatches, writes, evaluations, and other control decisions can be recorded against stable actors, services, sessions, and workflows.
Evaluates work before and after execution
The Control Plane supports inline checks, policy evaluation, structured validation, model-based review, and replay-driven grading so teams can evaluate whether workflows are operating correctly.
Built for real enterprise workflows
The Control Plane is designed for environments where AI workflows must be explainable, reviewable, and enforceable.
In real enterprise work, a single session may retrieve context, query structured data, use a tool, create an artifact, call another service, and write memory for future workflows. Each of those steps can carry policy implications.
The Control Plane applies governance across those transitions.
It combines access control, taints, manifests, evaluation, lineage, and audit into a shared control model. That gives teams a way to understand not only what the AI system produced, but what it saw, what it was allowed to do, what it was prevented from doing, and why.
This moves governance from documentation to execution.
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