Building Structured Inter-Agent Communication: A Practical Guide

In the realm of multi-agent systems, establishing reliable communication between agents can be a challenging task. While numerous tutorials demonstrate how one agent communicates with another, they seldom address the critical issue of maintaining this communication at large scales.

One particular problem arises when dealing with string-based agent chat. For instance, consider the following code snippet:

result = agent_a.run( Analyze this and tell agent_b what to do )

agent_b.run(result)

As you can see, the issue becomes evident when the 'result' contains a substantial number of tokens, possibly exceeding the token limits. Moreover, if the output omits crucial context or if critical parameters are simply summarized away, it can lead to discrepancies in how agent B interprets the instructions, resulting in incorrect inferences.

The solution proposed in this case is the implementation of Typed JSON Contracts. This approach requires every agent in AgentForge to declare its input schema. For instance, an input schema might look like this:

{

agent: "risk_analyzer",

input: {

portfolio: ["AAPL", "TSLA"],

timeframe: "1d",

risk_threshold: 0.05

},

expected_output: {

max_drawdown: float,

sharpe_ratio: float,

flags: [string]

}

}

Once the schema is defined, an orchestrator can be used to validate the output from agent A before it is passed to agent B. If agent A's output does not match agent B's expected input schema, the pipeline will halt and provide a clear error message rather than resulting in incorrect inferences.

The schema enforcement at runtime is achieved using the following code:

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 score of "high" instead of the expected value of 0.92, the orchestrator will flag this immediately, ensuring deterministic, debuggable, and testable behavior in production environments.

This approach, built with AgentForge, is open source and has been production-tested. The question remains, however, whether to enforce schemas in agent pipelines or to blindly trust the LLM to figure it out. This is a decision that needs to be made carefully, considering the importance of reliability and precision in multi-agent systems.