# Building Structured Inter-Agent Communication: A Practical Guide

> Source: <https://dev.to/albert_zhang_f468830cf0e6/building-structured-inter-agent-communication-a-practical-guide-2b5k>
> Published: 2026-06-17 11:00:32+00:00

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.*