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Multi-agent systems
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Multi-agent systems
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Agent markets: 85% of the loss comes before negotiation
Anthropic's book market experiment locates most efficiency loss in preference representation. A look at model choice, preference evals, and market rules.
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Anthropic Agent 市场:85% 的效率损失发生在谈判前
Anthropic 的 Agent 市场实验显示,主要效率损失发生在偏好表示和匹配阶段。本文拆解模型选择、偏好 Eval 与市场规则。
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Claude takes a candidate to the lab, not a new CRISPR
About 950 Claude agents helped identify ART. Human wet-lab experiments confirmed short RNA expression, but its main biological function remains unknown.
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Claude 把候选推进湿实验,但这不是新 CRISPR
Anthropic 用约 950 个 Claude Agent 从大规模逆转录酶候选中筛出 ART,并由人类湿实验验证短 RNA 表达,但 ART 的主要功能仍未知。
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When to split a task across agents: check dependencies
How task dependencies shape multi-agent results, with SAIGE experiments, ablations, and OpenAI guidance for deciding when to delegate.
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一个任务什么时候该拆成多个 Agent?先看依赖关系
Rethinking Multi-Agent Collaboration 说明多 Agent 的收益取决于任务依赖结构。本文结合 SAIGE 主实验、消融实验和 OpenAI 多 Agent 工程建议,整理可执行的委派判断。
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AWS agents propose; deterministic code validates
How AWS separates agent proposals, rule checks, human approval, and execution, with code enforcing boundaries and idempotency.
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OpenCompany: version-controlled agent configuration
OpenCompany uses company.toml, agent teams, and human sign-off to put organizational structure and agent configuration into version-controlled files.
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OpenCompany:把公司写成可版本控制的 Agent 配置
OpenCompany 用 company.toml、Agent Team 和 Human Sign-off 把组织结构与 Agent 配置写成可版本控制文件,并明确保留人工签字点。
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jcode moves coding-agent bottlenecks into the harness
How jcode handles multi-agent resources, state synchronization, and verification in its harness, beyond the capabilities of the model alone.
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jcode:把 Coding Agent 的瓶颈从模型转到 Harness
介绍 jcode 如何把多 Agent 的资源、状态同步和验证放到 Harness 层,说明并行 Coding Agent 的瓶颈不只是模型能力。
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AI Engineering Harness: verifiable delivery for AI code
A multi-agent engineering workflow uses isolated worktrees, CI, cold-start review, evidence gates, and layered context to control delivery of AI-generated code.
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AI Engineering Harness:把 AI 代码放进可验证的工程闭环
介绍一个多 Agent 工程工作流,如何用独立 worktree、CI、冷启动审查、证据门和分层上下文控制 AI 生成代码的交付。
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Building WishLive: A Multi-Agent Runtime
A 48-hour hackathon build of an event-driven multi-agent runtime with registry, workflow, execution, negotiation, settlement, and observability layers.
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