{"slug": "building-structured-inter-agent-communication-a-practical-guide", "title": "Building Structured Inter-Agent Communication: A Practical Guide", "summary": "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.", "body_md": "Every multi-agent tutorial shows \"Agent A talks to Agent B.\" None show *how* to keep that conversation reliable at scale.\n\n```\n# What most frameworks do:\nresult = agent_a.run(\"Analyze this and tell agent_b what to do\")\nagent_b.run(result)  # What if result is 2000 tokens? What if it omits context?\n```\n\nThis breaks when:\n\nEvery agent in AgentForge declares its input schema:\n\n```\n{\n  \"agent\": \"risk_analyzer\",\n  \"input\": {\n    \"portfolio\": [\"AAPL\", \"TSLA\"],\n    \"timeframe\": \"1d\",\n    \"risk_threshold\": 0.05\n  },\n  \"expected_output\": {\n    \"max_drawdown\": \"float\",\n    \"sharpe_ratio\": \"float\",\n    \"flags\": [\"string\"]\n  }\n}\n```\n\nThe 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.\n\n``` python\nfrom agentforge.core import Orchestrator, AgentContract\n\ncontract = AgentContract(\n    input_schema={\"query\": str, \"max_results\": int},\n    output_schema={\"results\": list, \"confidence\": float}\n)\n\norch = Orchestrator()\norch.register(\"search_agent\", search_fn, contract)\n```\n\nIf `search_fn`\n\nreturns `\"confidence\": \"high\"`\n\ninstead of `0.92`\n\n, the orchestrator flags it immediately.\n\nIn production, you don't want agents to \"kind of work.\" You want deterministic, debuggable, testable behavior. Typed contracts give you that.\n\n**Built with AgentForge.** Open source. Production-tested.\n\n[https://github.com/agentforge-cyber/agentforge-mvp](https://github.com/agentforge-cyber/agentforge-mvp)\n\n**Do you enforce schemas in your agent pipelines? Or do you trust the LLM to \"figure it out\"?**\n\n*Posted on 2026-06-17 by the AgentForge team.*", "url": "https://wpnews.pro/news/building-structured-inter-agent-communication-a-practical-guide", "canonical_source": "https://dev.to/albert_zhang_f468830cf0e6/building-structured-inter-agent-communication-a-practical-guide-2b5k", "published_at": "2026-06-17 11:00:32+00:00", "updated_at": "2026-06-17 11:21:26.289336+00:00", "lang": "en", "topics": ["ai-agents", "developer-tools", "large-language-models"], "entities": ["AgentForge", "Orchestrator", "AgentContract"], "alternates": {"html": "https://wpnews.pro/news/building-structured-inter-agent-communication-a-practical-guide", "markdown": "https://wpnews.pro/news/building-structured-inter-agent-communication-a-practical-guide.md", "text": "https://wpnews.pro/news/building-structured-inter-agent-communication-a-practical-guide.txt", "jsonld": "https://wpnews.pro/news/building-structured-inter-agent-communication-a-practical-guide.jsonld"}}