Protocol Positioning

While other initiatives focus on providing agents with tool access or enabling basic communication between agents, ACP focuses exclusively on the governance layer: authorization, policy enforcement, execution verification, and institutional responsibility.

Comparison Table

Protocol / Framework	Primary Focus	Primary Limitation	ACP's Approach
MCP (Model Context Protocol)	Standardized tool access for LLMs	Does not enforce authority verification or robust execution governance across institutions.	Provides the verifiable governance wrapper around systems like MCP, ensuring only authorized capabilities execute.
A2A (Agent-to-Agent)	Standardizing routing and communication between autonomous agents.	Does not address the underlying institutional accountability or policy constraints for requested actions.	Focuses entirely on the governance of the requested action, ensuring traceable institutional roots.
OpenAI Agents SDK	Development and orchestration of autonomous agents within the OpenAI ecosystem.	Lacks native cross-institution governance, cryptographic identity verification, and vendor-agnostic policy validation.	Operates independently of the underlying agent framework, acting as a standardized governance gateway for all agents.
Agent Client Protocol	Client-side runtime integration and agent UI embedding.	Lacks backend execution verification, immutable logging, and multi-party trust layers.	Builds the backend execution, verification, and accountability layers necessary for enterprise integration.
ACP (Agent Control Protocol)	Governance, Traceability, Institutional Accountability	Does not orchestrate tools or handle LLM reasoning natively.	Works vertically alongside existing orchestrators to add mandatory governance layers.