Fallacies of GenAI Development #8: More AI Agents Means More Productivity
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Critical analysis of AI agent productivity myths, directly relevant to multi-agent systems.
Summary
Adding more AI agents to a codebase without coordination protocols creates a distributed systems problem, not linear productivity gains. Each agent makes local decisions (naming conventions, retry strategies, error handling) that silently conflict, leading to integration failures, thundering herds, and architectural drift by months 5-8. The fallacy mirrors the lesson from distributed systems: scaling requires protocols (consensus, conflict resolution, interface contracts), not just more nodes.