AI agent protocols: what governance gap are teams missing?

TL;DR: AI agent protocols such as MCP, A2A, ANP, ACP and LCEL are emerging to standardise how agents exchange information, coordinate actions and handle errors across cybersecurity use cases, according to Black Kite. The deeper issue is that agent collaboration creates governance, identity and trust problems that conventional automation models were never built to manage.

Editorial analysis by NHI Mgmt Group, based on content published by Black Kite: “AI Agents and Protocols:”.

Key questions

Q: How should security teams govern agentic AI as it moves into production?

A: Security teams should govern agentic AI as a class of non-human identity, not as a generic application feature.

Q: Why do AI agents create new risk in non-human identity management?

A: AI agents create risk because they operate as software identities with delegated authority, but many organisations do not track them with the same discipline applied to users or service accounts.

Q: What breaks when multiple agents share tools and data without a common protocol?

A: The failure mode is inconsistent coordination: agents can duplicate work, act on stale context or pass unsafe decisions downstream.

Practitioner guidance

• Define protocol-level trust boundaries Specify which systems, tools and peer agents each protocol may reach, and require explicit approval for any cross-domain or cross-environment interaction.
• Bind each action to a governed agent identity Assign a distinct identity to each agent, record its delegated scope and revoke access when the task, workflow or session ends.
• Test for cascading failure across agent chains Simulate stale context, conflicting instructions and malformed messages to see how far a bad decision can propagate through collaborating agents.

Bottom line: AI agent protocols solve coordination problems, but they also expose where governance for identity, delegation and trust is still immature.

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