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…
Building Structured Inter-Agent Communication: A Practical Guide
Every multi-agent tutorial demonstrates one agent communicating with another. However, few tutorials explain how to maintain reliable conversations between agents as they scale. The Issue with String-Based Agent Chat
Most frameworks operate by having agent A execute a function, passing it data, and then agent B processes the output. This approach falters when the output exceeds token limits or omits crucial context. When agents interpret instructions differently, the system breaks down.
Our Solution: Typed JSON Contracts
AgentForge addresses this problem by implementing Typed JSON Contracts. Each agent declares its input schema, such as:
{
"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 the output against the input schema before proceeding. If agent A's output doesn't match agent B's expected input, the pipeline halts with a clear error, preventing incorrect inferences.
Runtime Schema Enforcement
AgentForge provides Orchestra and AgentContract classes. Here's an example:
```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 the search_fn returns a confidence level different from what the contract expects (e.g., "high" instead of 0.92), the orchestrator flags the issue immediately. This ensures deterministic, debuggable, and testable behavior in agent pipelines.
Why This Matters
In production environments, agents should not "kind of work." Typed contracts provide the necessary structure for reliable, reproducible interactions between agents. AgentForge offers an open-source, production-tested solution for implementing these contracts.
The AgentForge team encourages readers to consider schema enforcement in their agent pipelines, questioning whether they rely solely on LLMs to "figure it out" or if they adopt a more structured approach.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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