Girish Konda compares orchestrated subagentsA multi-agent system contains multiple AI agents that interact, coordinate, divide work, or influence one another while pursuing tasks. with distributed systems, identifying partial failures, shared-context contamination, unbounded fanout and repeated side effects. A fluent response alone, he argues, does not prove that a worker completed its task.
Girish Konda proposes source-backed facts, at-most-once effects, bounded fanout and explicit outcomes as a handoff modelAn agent handoff transfers an AI task, relevant context, authority, and responsibility between agents, people, sessions, or environments.. He illustrates the tradeoffs with examples from a supervised MCP bridge and explains why a timeout should remain an unknown outcome until checked.
Girish Konda describes the model’s limits: it cannot make source facts true, guarantee global ordering or automatically undo partial work. He recommends applying checks in code and choosing consistency and availability according to the operation.
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