「说 Harness 会被淘汰的，肯定没做过工程」，Kimi 前 CLI 负责人戳破了 AI 圈最大的误解
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Merged summary
TL;DR - Former Kimi CLI lead Richard Qian argues that stronger models will not eliminate agent harnesses; instead, complexity will shift from compensating for model weaknesses to coordinating multi-agent systems. This matters because state, memory, permissions, communication, and governance remain systems-engineering problems even as individual agents improve.
- Kimi CLI reportedly removed dedicated subagent scheduling and native parallel-tool controls once models could orchestrate tasks through shell scripts.
- More capable agents created higher-level requirements: cross-session state, proactive context management, task handoffs, communication protocols, and dynamic permissions.
- Qian defines a harness as the runtime control layer covering execution loops, context and state, tool and resource scheduling, and safety boundaries.
- His startup Raft focuses on this higher-level layer, coordinating persistent agents from different vendors in shared, auditable workflows rather than building another single-agent runtime.
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「说 Harness 会被淘汰的，肯定没做过工程」，Kimi 前 CLI 负责人戳破了 AI 圈最大的误解
TL;DR - Former Kimi CLI lead Richard Qian argues that stronger models will not eliminate agent harnesses; instead, complexity will shift from compensating for model weaknesses to coordinating multi-agent systems. This matters because state, memory, permissions, communication, and governance remain systems-engineering problems even as individual agents improve.
- Kimi CLI reportedly removed dedicated subagent scheduling and native parallel-tool controls once models could orchestrate tasks through shell scripts.
- More capable agents created higher-level requirements: cross-session state, proactive context management, task handoffs, communication protocols, and dynamic permissions.
- Qian defines a harness as the runtime control layer covering execution loops, context and state, tool and resource scheduling, and safety boundaries.
- His startup Raft focuses on this higher-level layer, coordinating persistent agents from different vendors in shared, auditable workflows rather than building another single-agent runtime.