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 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"]
  }
}

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)

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 2026-07-03 by the AgentForge team.