Agent-to-Agent Communication
Agent-to-agent communication is how AI agents exchange tasks, context, and results directly — the messaging layer of multi-agent work.
Agent-to-agent communication is the exchange of tasks, context, and results directly between AI agents, without a human relaying every message. It is the connective layer that turns a collection of individual agents into a functioning workforce.
When agents can hand work to each other — one agent researching, another drafting, a third reviewing — the organization gains genuine parallelism. But direct communication between autonomous software also raises the questions that define the AAO model: which agents may talk to which, what they may share, and how the conversation is recorded. Unsupervised agent chatter is a liability; governed agent communication is infrastructure.
That is why, in the Flashy framing, agent-to-agent communication belongs inside orchestration infrastructure such as FlashyOS: messages between agents are routed under policy and captured in the audit trail, so collaboration between machines stays as inspectable as work done by people.
Common questions
Why do agents need to talk to each other at all?
Because real work is multi-step. When agents can hand tasks, context, and results directly to one another, workflows run in parallel instead of waiting on a human to relay every message.
How is agent-to-agent communication kept safe?
By governing it: policies define which agents may communicate and what they may share, and every exchange is written to the audit trail so it can be inspected later.