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A2A — agent-to-agent — is an open protocol for AI agents to discover each other, exchange tasks, and collaborate across different vendors and frameworks. Where MCP connects an agent to tools and data, A2A connects agents to other agents, enabling multi-agent workflows: one agent researching, another drafting, another checking rights, each contributing to a shared task.
Content work is naturally multi-agent: a request like "prepare the launch kit for market X" decomposes into finding approved assets, checking market-level rights, drafting localized copy, generating renditions, and routing for approval. With A2A, specialized agents hand these steps to each other with task state and results flowing between them — including agents built by different vendors — rather than one monolithic bot attempting everything. The content platform participates as both a tool provider (via MCP) and, increasingly, as a home for its own agents that other agents can delegate to.
Multi-agent workflows multiply the access question: which agent, acting for which user, may touch which assets? Platforms built for regulated environments answer it the same way they answer it for humans — every agent action executes under an authenticated identity, against asset-level permissions, and lands in the audit log. Without that, agent collaboration is a compliance incident generator; with it, it is simply faster teamwork.
ioMoVo ships A2A alongside MCP and its API: ioPilot's multi-agent tools collaborate on content tasks — and external agents can participate — with every action permission-checked and audit-logged, deployable to fully air-gapped environments. See the ioPilot page.
MCP is agent-to-tool (an agent using the DAM's search, for example); A2A is agent-to-agent (two agents dividing a task between them). Mature AI infrastructure uses both.
Yes, when the platform, the models (via BYOLLM), and the protocols all run inside the boundary — which is the requirement for sovereign and defense use.