「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 stdrc (Richard Qian), now founder of Raft, argues against the popular claim that agent "harnesses" will be absorbed into models — instead, stronger models push harness complexity upward, from capability patching to multi-agent coordination. It matters because it reframes where durable engineering value sits in agentic systems.
- Core thesis: harness thickness doesn't shrink, it migrates. Low-level "patch" harness (format constraints, tool-use prompts, retry logic) disappears as models improve, while upper-layer needs (multi-agent handoff, cross-session state sync, proactive memory management, dynamic permissions, cross-vendor protocols) grow.
- Full harness definition offered: a runtime engineering control layer around the model with four elements — agent execution loop, context/state management, tool & resource scheduling, and safety/boundary governance — not just "system prompt + tool wrappers."
- Kimi CLI evidence: the team deleted its dedicated subagent scheduler and native parallel tool-calling, letting the model do task splitting via bash and generated scripts. It worked past a capability threshold — but immediately surfaced harder inter-agent communication and task-handoff problems.
- Raft as the applied bet: no agent loop or base tool wrappers of its own; it plugs in Claude Code, DeepSeek, etc. as "team members," adding per-agent identity/memory/process, channel-based task claiming and handoff with audit trails, and a cross-vendor protocol. Priced at 0.1 human seat per agent. Framing is the author's own; no benchmarks or results are presented.
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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 stdrc (Richard Qian), now founder of Raft, argues against the popular claim that agent "harnesses" will be absorbed into models — instead, stronger models push harness complexity upward, from capability patching to multi-agent coordination. It matters because it reframes where durable engineering value sits in agentic systems.
- Core thesis: harness thickness doesn't shrink, it migrates. Low-level "patch" harness (format constraints, tool-use prompts, retry logic) disappears as models improve, while upper-layer needs (multi-agent handoff, cross-session state sync, proactive memory management, dynamic permissions, cross-vendor protocols) grow.
- Full harness definition offered: a runtime engineering control layer around the model with four elements — agent execution loop, context/state management, tool & resource scheduling, and safety/boundary governance — not just "system prompt + tool wrappers."
- Kimi CLI evidence: the team deleted its dedicated subagent scheduler and native parallel tool-calling, letting the model do task splitting via bash and generated scripts. It worked past a capability threshold — but immediately surfaced harder inter-agent communication and task-handoff problems.
- Raft as the applied bet: no agent loop or base tool wrappers of its own; it plugs in Claude Code, DeepSeek, etc. as "team members," adding per-agent identity/memory/process, channel-based task claiming and handoff with audit trails, and a cross-vendor protocol. Priced at 0.1 human seat per agent. Framing is the author's own; no benchmarks or results are presented.