Codex、Claude、AutoGen都能并行Agent，为什么项目反而更容易失控？｜APEX断点EP03

This episode explores why parallel multi-agent systems (Codex, Claude, AutoGen) often become harder to control despite their capabilities. The core insight is that multi-agent value comes from controlled division of labor. The discussion uses official capabilities of these platforms as entry points, analyzing how the same model, inputs, and tasks can lead to loss of control when agents operate in parallel without proper coordination mechanisms.

Key Points

• •Multi-agent value derives from controlled task division, not just parallel execution
• •Parallel agents without coordination mechanisms increase system complexity and reduce controllability
• •Codex, Claude, and AutoGen each have different parallelization approaches with distinct trade-offs
• •Same model + same input + same task can produce divergent outputs in parallel scenarios, requiring synchronization strategies
• •Agent communication protocols and state management are critical for maintaining control in multi-agent systems
• •Uncontrolled agent proliferation leads to emergent behaviors that are difficult to predict or debug
• •Effective multi-agent design requires explicit orchestration layers and clear responsibility boundaries
• •Testing and validation become exponentially harder as agent count and interdependencies increase

Workflow Diagram

Concepts