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构建结构化的 Agent 间通信：实用指南
文章指出多数多 Agent 教程只演示“Agent A 与 Agent B 对话”，未解决规模化下的可靠性问题：输出超出 token 限制、关键参数被摘要掉、下游 Agent 解析偏差。作者在 AgentForge 中为每个 Agent 声明输入 schema，由编排器在执行前校验，不匹配则中止并报错，而非让下游 Agent 做出错误推断。
AI推荐理由 文章针对多 Agent 系统中基于字符串传递消息的可靠性问题，提出用带类型的 JSON 契约声明各 Agent 的输入输出 schema，并由编排器在执行前校验，避免输出超长、关键参数被摘要掉或解析歧义。属于工程实践分享，含代码示例，但来自个人博客，缺少规模化验证与对比数据。
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正文
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?
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This breaks when:
Output exceeds token limits
Critical parameters get "summarized" away
Agent B parses instructions differently than intended
Our Solution: Typed JSON Contracts
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"]
}
}
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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.
Schema Enforcement at Runtime
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)
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If search_fn returns "confidence": "high" instead of 0.92, the orchestrator flags it immediately.
Why This Matters
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
Do you enforce schemas in your agent pipelines? Or do you trust the LLM to "figure it out"?
Posted on 2026-09-13 by the AgentForge team.