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An AI agent control plane is a single inline enforcement layer that applies the same deterministic policy engine, probabilistic risk scoring, and tamper-evident audit log across every AI agent protocol. This guide explains why each protocol’s native controls are insufficient on their own, presents Vaikora’s protocol-agnostic enforcement architecture.

ACP (Agent Communication Protocol) and ANP (Agent Network Protocol) are the two AI agent protocols most teams encounter after MCP and A2A. This guide defines each acronym on first use, covers their architectures with example payloads, ends with a decision matrix mapping project types to protocol fit, and shows where Vaikora applies a single enforcement layer across both.

You stop PII from leaking between AI agents by placing a deterministic policy enforcement layer (with probabilistic risk scoring) inline on the A2A task hand-off, so every task message is inspected, classified, and either redacted or blocked before it reaches the Remote-Agent. Concretely, A2A traffic flows through Vaikora as a transparent egress

A2A defines a Task-Based Actor Model — a User sends work to a Client-Agent, which then delegates to one or more Remote-Agents — and a discovery mechanism based on agent cards published at /.well-known/agent.json. This guide shows how Vaikora applies inline policy enforcement on every A2A task message before it crosses an organizational boundary.

MCP is the answer to a simple question: how does an LLM call a tool, read a database, or open a file in a way that any compliant AI application can consume? This guide explains the architecture, the transport layer, and three concrete use cases — and shows where a runtime control layer like Vaikora fits.

MCP is the answer to a simple question: how does an LLM call a tool, read a database, or open a file in a way that any compliant AI application can consume? This guide explains the architecture, the transport layer, and three concrete use cases — and shows where a runtime control layer like Vaikora fits.

I agent protocols are the standard ways autonomous AI systems discover services, exchange messages, and call tools across organizations. The four protocols that matter today are MCP (Model Context Protocol), A2A (Agent-to-Agent), ACP (Agent Communication Protocol), and ANP (Agent Network Protocol).

This article explains how AI agent activity can be surfaced in AWS Security Hub using Vaikora. Vaikora’s capabilities include providing deep visibility into AI agent operations and generating comprehensive audit logs for real-time monitoring, compliance tracking, and threat analysis.

Most CrowdStrike deployments have the same blind spot. Endpoints are covered, IAM behavior is logged, network traffic is monitored. But the AI agents running on that infrastructure, making thousands of decisions per day, generate zero signals in Falcon unless something hits the endpoint in a way that looks like traditional malware.