Runtime governance for AI agents

The problem

Agent frameworks were designed for capability, not containment. An agent is typically given a credential, a set of tools and an objective, and then left to decide the sequence itself. The approval happened once; the actions happen continuously.

That gap is not a model-quality problem. A perfectly aligned agent still needs an answer to a simple operational question: is this specific call, against this specific system, with this specific data, permitted right now?

Why runtime matters here more than anywhere

Agents compose actions faster than review cycles operate. By the time a weekly control review notices a pattern, the agent has completed thousands of invocations.

Agents also chain. One agent calls another, which calls a tool, which reaches a dataset. Governance attached to the first request tells you nothing about the third hop unless it travels with the action.

How Skipr fits

AgentConnect gives each agent a cryptographic identity of its own, separate from the human or service that launched it, and puts every tool call, MCP invocation and agent-to-agent exchange on the governed path.

IntelConnect supplies the context for the decision, DataConnect governs what information the agent may reach, and the Evidence stage records the outcome. Revoking an agent's identity stops the agent without disturbing its operator.

Questions

Why isn't a system prompt or guardrail enough?

Guardrails shape what a model says. Runtime governance decides what a system does. A refusal in text does not prevent a tool invocation that is already permitted by the credential the agent holds.

Does per-action authorization slow agents down?

The decision is made in-path and scoped to the call. The design goal is that governance is part of the execution path rather than a separate review queue that stops work altogether.

How are agents identified?

As first-class principals with their own cryptographic identity, scope and lifetime — not by borrowing a human's session or a shared service account.

What about MCP and agent-to-agent protocols?

They are treated as governed paths. The protocol carries the request; the control plane still decides whether the request executes.

Can a human be required in the loop?

Yes. Escalation and human approval are possible decision outcomes at the Decision stage, alongside allow, deny and route.