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?

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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" ] } }

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The orchestrator validates before execution.…