How multiple AI agents pass information and delegate tasks to each other — the protocols, patterns and risks behind coordinated multi-agent systems.
When multiple AI agents work together on a complex task, they need a way to pass instructions, share intermediate results and delegate sub-tasks to one another. Agent-to-agent communication is the mechanism that makes this possible — a combination of structured message formats, capability discovery and orchestration logic that allows specialised agents to collaborate without each needing to understand the full problem. For enterprises building sophisticated AI workflows, the quality of this communication layer determines whether a multi-agent system is reliable or brittle.
In a coordinated AI system, agents are not independent models each doing their own thing — they form a network where one agent (often called an orchestrator or planner) decomposes a goal into sub-tasks and assigns them to specialised agents. Each sub-agent executes its portion and returns a result. The orchestrator synthesises those results, determines what comes next, and may dispatch further delegations.
Communication between agents happens through structured messages — typically JSON payloads — that carry task descriptions, relevant context, constraints and expected output formats. A well-designed system defines these message schemas explicitly, so agents do not misinterpret instructions or produce outputs in formats other agents cannot parse.
The business case for multi-agent coordination is straightforward: complex enterprise tasks — end-to-end procurement, multi-system financial reconciliation, cross-departmental reporting — exceed what a single AI agent can reliably handle in one pass. Breaking the task across specialised agents mirrors how human teams divide expert labour.
According to Anthropic's 2026 enterprise AI adoption research, 57% of organisations now deploy agents for multi-stage workflows, and 16% have achieved cross-functional, end-to-end process automation. The organisations reaching those more sophisticated deployments are precisely the ones that have invested in robust agent coordination patterns, not just individual model quality.
For Australian mid-market organisations, the practical benefit is that well-coordinated agent systems can handle operational workflows with a level of consistency and auditability that ad hoc AI use cannot match.
Agent-to-agent communication operates at two levels: the message protocol and the capability discovery mechanism.
Message protocol: Agents exchange messages with a defined structure — typically containing a task identifier, instruction payload, relevant context (prior results, constraints), and metadata about the expected response format. The orchestrating agent appends results from previous steps, building a shared context as the workflow progresses.
Capability discovery: For agents to delegate intelligently, they need to know what other agents can do. Some systems use static registries — a manifest listing each agent and its capabilities. More sophisticated implementations use dynamic discovery, where agents advertise capabilities at runtime. Google's A2A (Agent-to-Agent) protocol formalises this: agents publish an "agent card" describing their skills, and orchestrators query these cards when routing tasks.
Handoff patterns: Common coordination patterns include sequential chains (Agent A → Agent B → Agent C), parallel fan-out (the orchestrator delegates to multiple agents simultaneously and waits for all results), and conditional routing (the next agent is selected based on the output of the previous step).
Each pattern has different implications for latency, error handling and consistency that architectural decisions need to account for.
Building reliable agent communication requires more than choosing a protocol. Several practical decisions shape outcomes significantly.
Message validation is non-negotiable. When an agent passes a result to the next agent in a chain, that result should be validated against the expected schema before it is processed. Without this, a malformed output from one agent causes cryptic failures downstream.
Scope isolation matters for security. An agent should only have access to the context and tools relevant to its specific task — not the entire state of the system. This principle of least privilege limits the blast radius if any single agent behaves unexpectedly or is manipulated through a malicious input.
Logging must capture inter-agent messages, not just final outputs. When a multi-agent workflow produces an incorrect result, diagnosing the source requires a complete trace of what each agent received, what it produced, and how that was passed forward. Edison AI's AI implementation engagements typically include a specific observability design phase for multi-agent workflows, precisely because this tracing requirement is so frequently underestimated.
For organisations currently using single agents or simple automation, the path toward multi-agent coordination should be incremental. Identify one high-value workflow that genuinely requires multiple specialised capabilities, design the message schemas explicitly before building, and instrument full tracing from day one. Only expand to additional workflows once the first system operates reliably with clear audit trails and defined failure handling.
Edison AI designs and ships AI agents and workflow automation built around how your business actually runs.
Agents exchange structured messages — typically JSON payloads — containing task instructions, context, intermediate results or requests for specific capabilities. An orchestrating agent breaks a goal into sub-tasks and delegates them to specialised sub-agents, which return results that the orchestrator incorporates into its next reasoning step.
A2A (Agent-to-Agent) is an emerging open protocol, developed by Google and partners, that defines how agents advertise capabilities and exchange tasks across different frameworks and vendors. It complements MCP — MCP connects agents to tools, while A2A connects agents to other agents.
The main risks are error propagation (a mistake in one agent cascades through the chain), prompt injection via inter-agent messages, and runaway task loops. Robust systems include message validation, explicit scope limits and human-in-the-loop checkpoints at high-stakes decision points.
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Article: Agent-to-Agent Communication: Protocols Behind Coordinated AI Systems
