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17 articles from Crelis on Model Context Protocol. Most recent: “Agentic AI Controls: A Working Definition”.
Agentic AI controls are the limits, checks, approvals, and records that decide what an agent may do before it changes money, data, access, or records.
Agent security maps help only if they lead to evidence for the permission behind a consequential action.
Transparency can explain that AI was involved. It does not prove that an agent had authority to release money, delete a record, or change a credit limit.
MCP approval can move a workflow forward, but the specification text separates transport authorization, elicitation, and later evidence of who had authority for a particular tool call.
MCP authorization uses OAuth to govern access to a server, but it does not prove why a payment release or record deletion was allowed.
MCP Multi Round-Trip Requests make approval easier to place in the request path, but they do not preserve the evidence behind an authority decision.
MCP server guidance can reduce unsafe calls, but the audit problem is proving who authorised the action before money, records, or tools changed.
OWASP maps agentic AI risks; it does not prove who authorised a payment, record change, or refund.
Identity tells you who an AI agent is. Authorization tells you it was allowed to act. Evidence tells you that you can still prove both later, to someone who does not trust your dashboard, and that thi
Runtime security asks whether an AI agent's activity is a threat. Runtime authorization asks whether the action was permitted — and whether you can prove who permitted it.
Blocking a bad action proves nothing about the good ones you allowed. Enforcement stops an action; evidence proves, afterwards, that an action was authorized and by whom — and almost no one sells the
MCP can authorize an individual tool call and can pause mid-call for a human decision; what it cannot do is record which policy permitted the action, or keep that record after the call returns.
Dynatrace found that nearly half of organizations discard log data, excluding an average of 86% of it. Tamper-evident AI audit logs turn passive monitoring into proof of authorized execution.
Understanding a model's logic is not a legal defence. The industry is moving from model explainability to verifiable proof of what an agent actually did.
An autonomous agent operating without a clinical oversight protocol is a liability, not an asset. In high-stakes environments, the gap between an AI proposal and a finalized execution is where enterpr
Traditional policy frameworks cannot govern non-deterministic agentic systems. Paper-based compliance is dead, and selecting the right AI compliance platform is now a matter of legal survival.
Intelligence isn't authority. A model's capacity to act matters less than your ability to prove why it acted — and what the record has to contain to do that.