Building Structured Inter-Agent Communication: A Practical Guide

The AgentForge team introduced typed contracts for inter-agent communication to ensure reliable, deterministic behavior in multi-agent systems. The framework validates input and output schemas before execution, halting pipelines with clear errors if mismatches occur. This approach aims to replace the common but fragile pattern of trusting LLMs to 'figure out' unstructured messages.

Every multi-agent tutorial shows "Agent A talks to Agent B." None show how to keep that conversation reliable at scale. 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: Every agent in AgentForge declares its input schema: { "agent": "risk analyzer", "input": { "portfolio": "AAPL", "TSLA" , "timeframe": "1d", "risk threshold": 0.05 }, "expected output": { "max drawdown": "float", "sharpe ratio": "float", "flags": "string" } } The orchestrator validates before execution. If agent A's output doesn't match agent B's input schema, the pipeline halts with a clear error — instead of agent B making a wrong inference. python from agentforge.core import Orchestrator, AgentContract contract = AgentContract input schema={"query": str, "max results": int}, output schema={"results": list, "confidence": float} orch = Orchestrator orch.register "search agent", search fn, contract If search fn returns "confidence": "high" instead of 0.92 , the orchestrator flags it immediately. In production, you don't want agents to "kind of work." You want deterministic, debuggable, testable behavior. Typed contracts give you that. Built with AgentForge. Open source. Production-tested. https://github.com/agentforge-cyber/agentforge-mvp https://github.com/agentforge-cyber/agentforge-mvp Do you enforce schemas in your agent pipelines? Or do you trust the LLM to "figure it out"? Posted on 2026-06-17 by the AgentForge team.