Handoffs and Messages

Relay races are won and lost at the baton exchange. The fastest runners in the world can lose a race if the handoff is fumbled — the baton dropped, the timing off, the next runner not ready. The same is true in multi-agent AI systems. Each agent may do its individual job perfectly, but if the information passed between agents is incomplete, mis-formatted, or misunderstood, the whole system fails. The handoff — the moment when one agent's output becomes another agent's input — is one of the most critical design points in any multi-agent architecture.

What a Handoff Is

A handoff occurs when one agent completes its portion of a task and passes the result — along with any context the next agent needs — to another agent or back to the supervisor. The receiving agent uses that information to pick up exactly where the sending agent left off. A handoff is not just a copy-paste of raw output. It is a structured transfer that typically includes: the result itself (the research summary, the draft paragraph, the completed calculation), the context the next agent needs (what the overall goal is, what has already been done, what constraints apply), and instructions for the receiving agent (what it should do next with this information). Without all three components, the receiving agent may misinterpret its task, redo work already done, or produce output that does not fit the larger picture.

Messages: The Language of Agent Communication

Agents communicate through messages — structured pieces of data passed between agents or between an agent and the supervisor. In most modern multi-agent frameworks, messages are formatted as structured text or as objects with defined fields: sender, recipient, task type, content, and any relevant metadata. Think of a message as an inter-office memo that is precise enough for a computer to parse. Unlike a casual conversation, agent messages must be unambiguous. If a message says 'make it better,' the receiving agent does not know what better means. A good message says: 'Revise the following draft to reduce word count by 20% and use simpler vocabulary for a sixth-grade reading level.' Clear, specific messages are the foundation of reliable agent communication. Vague messages produce unpredictable results.

A common format used in multi-agent systems is a context package — a bundle of information assembled by the supervisor or the sending agent that contains everything the receiving agent needs. A well-designed context package prevents agents from having to ask questions mid-task, which slows down the whole system and introduces new opportunities for errors.

Context and Memory Across Handoffs

One of the trickiest challenges in multi-agent systems is memory. Each individual AI agent typically only sees what is passed to it — it does not automatically remember everything that happened earlier in the pipeline. If the supervisor forgets to include important context in a handoff, the receiving agent is working blind. Imagine a research agent discovers that one source is unreliable and flags it in its output. If the supervisor strips out that flag when passing the research to the writing agent, the writing agent may cite the unreliable source without knowing it is problematic. Important information must travel with the task through every handoff. Good multi-agent system design treats context as cargo — something that must be deliberately packed and carried, not something that flows automatically.