Building Structured Inter-Agent Communication: A Practical Guide
Every multi-agent tutorial shows "Agent A talks to Agent B." None show how to keep that conversation reliable at scale. The Problem with String-Based Agent Chat # What most frameworks do: result = agent_a . run ( " Analyze this and tell agent_b what to do " ) agent_b . run ( result ) # What if result is 2000 tokens? What if it omits context? This breaks when: Output exceeds token limits Critical…
The article "Building Structured Inter-Agent Communication: A Practical Guide" by the AgentForge team outlines a method for ensuring reliable communication between multiple artificial intelligence agents. Traditional tutorials often demonstrate how Agent A interacts with Agent B, but rarely address how to maintain the reliability of this conversation at a larger scale.
A significant issue arises when the output of one agent becomes too lengthy or omits crucial context, leading to problems when the subsequent agent, Agent B, attempts to process it. This can occur when the output exceeds the token limits or when critical parameters are summarized away. As a result, Agent B may parse instructions differently than intended, causing incorrect inferences to be made.
To address this, the authors propose the use of Typed JSON Contracts. Each agent in the AgentForge framework declares its input schema, including the expected data types for parameters like portfolio, timeframe, and risk_threshold. The orchestrator then validates the output of one agent to ensure it matches the input schema of the next agent. If there is a mismatch, the pipeline halts with a clear error message, preventing incorrect processing downstream.
The article provides an example of how this system works. The `AgentContract` class defines both the input and output schemas for a search agent. When the search function returns a confidence level, the orchestrator checks if it matches the expected float format. If Agent B returns a confidence of "high" instead of 0.92, the orchestrator flags the issue immediately, ensuring deterministic, debuggable, and testable behavior in agent workflows.
The authors emphasize that this approach provides the benefits of deterministic, testable behavior in production environments. Typed JSON contracts are built into the AgentForge framework, which is open source and production-tested. The authors conclude by asking readers whether they enforce schemas in their agent pipelines or if they rely on the LLM to figure things out on its own.
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