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? Enter fullscreen mode Exit fullscreen mode 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" ] } } Enter fullscreen mode Exit fullscreen mode The orchestrator validates before execution.…