Building Structured Inter-Agent Communication: A Practical Guide

#ai #api #engineering

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)