DeepSeek 电话 Harness团队只是发布 dsh-superagents，一个麻省理工学院许可的研究原型。 DeepSeek dsh-superagents是一个GitHub寄存器的主动编码agent. 它被包装为DeepSeek Harness的捆绑。 它听GitHub和，可选的飞舒。 它将允许的请求变成并行的工作项目，每个项目都属于自己的agent届会。 发行品与一个设计站一起运出，"探索超级-agent设计：走向更自主的Agent系统".
TL；达R
在麻省理工学院下执行的1项任务。 它使用哈尼斯简介设定的模型。
- 运行在：你自己的机器，作为专用的DeepSeek Harness配置。 需要节点.js 22.19+或24+,git 2.32+,Python 3.8+,tmux和GitHub CLI 2.48+.
- 性能：在DeepSeek的模拟中，它在吵闹的信息流下完成的任务远快于单片段的agent.
最佳：p95任务完成15.7分钟，单会场基线29.5分钟(约1.9x更快).
- 最糟糕的是：仅通过合成信息和模拟工具测试。 未测量实际编码质量。
DeepSeek dsh-超剂是什么？
GitHub agent是等待被问到的。 当被允许的人处理时，它会打开一个工作项目，称为车票。 每张票都在自己的会话和目录中运行。 结果回到了原来的对话，经常作为拉动请求。 一个本地的网页面板只列出需要一个人的东西。
README页面明确阐述了核心规则：尽可能少地关注人类，并给予尽可能多的价值。 哲学博士将其应用于行为。 agent确认一次，在工作时保持安静，只以结果，问题或失败再次写作。
DeepSeek为何打造超级agent.
DeepSeek团队认为主流的agents将1会话作为基本单元。 根据报告，据Ifeng报告，一个人只能一次积极跟踪3至5次。 人类的注意力而不是模型能力成为多重任务的瓶颈。
路由器-工人架构如何运作？
研究团队描述了两层。 一个长期运行的路由器处理交互和调度。 孤立的工人会议负责实际工作。
在原型中，这个流动是混凝土，按照域模型：
- 源代码触发器：GitHub或Feishu生成事件。
- Eval：决定一批触发器是否要求新工作。
- 车票：一个经授权的单位，任务不可改变，说明为何可以继续。
- 订阅：以后对拉动请求的评论没有再次提及就到达合适的工人。
主会话运行dsh-superagent-router预设，工具集较小。 每个工人经营dsh -超级代理工人。 壳牌命令在5秒后移动到背景，因此Workers继续接收消息，按照执行文件。
主动性的工作是门。 授权文件定义了3个接受模式：关闭、观察(默认)和自动。 在观察中，agent只记录它会接受什么。
研究团队还列举了原型之外的想法：一个外部的监视犬，存储和计算分离，以及一个同步的ReAct循环。
如何表现？
DeepSeek通过模拟分组聊天和GitHub CI流测试。 每次跑步都有约300条信息，其中约20条是真实的工作请求。 每个计划都进行了5次，每Ifeng对报告的陈述。
- Super-agent:p50为12.4分钟，p95为15.7分钟。
- 单会话加亚剂，中性提示：p9529.5分钟。 没有给出p50。
- 为什么？在中性提示下，基线在5次运行中称为0次副剂。
- 路由器载重：随着抵达者从每分钟约1人增加到40人，批量规模从1.0人增加到2.8人。 按要求的延迟从2.5秒上升到3.9秒。
一次绝缘取消了直接事件通过。 路由器的决定上升了约30%，缓存读信器上升了约37%，从23.23M增加到31.77M. 所有数字都是自报的，来自报告的第二次报告。
dsh-superagents 如何与其他 GitHub 编码 agents 相比？
关键外卖
- DeepSeek 发布 dsh-superagents，一种麻省理工学院许可的主动编码agent原型。
- 路由器发送工作；隔离的工人会执行每张票。
- 模拟p95任务时间：单场15.7分钟对29.5分钟。
- 积极主动的工作在观察模式下，直到一个团队打开它。
- 它运行与完整的OS用户访问，所以隔离它使用前。
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The DeepSeek Harness team just released dsh-superagent, an MIT-licensed research prototype. DeepSeek dsh-superagent is a proactive coding agent for one GitHub repository. It is packaged as a bundle for DeepSeek Harness. It listens to GitHub and, optionally, Feishu. It turns allowed requests into parallel work items, each in its own agent session. The release shipped alongside a design post, Exploring Super-agent Design: Towards a More Autonomous Agent System.
TL;DaR
- What is it?: A TypeScript bundle, v0.1.0, released as 1 commit under MIT. It uses whatever model the Harness profile is set to.
- Runs on: Your own machine, as a dedicated DeepSeek Harness profile. Needs Node.js 22.19+ or 24+, git 2.32+, Python 3.8+, tmux and GitHub CLI 2.48+.
- Performance: In DeepSeek’s simulation, it finished tasks far faster under a noisy message stream than a single-session agent.
- Best: p95 task completion of 15.7 minutes vs 29.5 minutes for the single-session baseline (about 1.9x faster).
- Worst: Tested only on synthetic messages and simulated tools. Real coding quality was not measured.
What is DeepSeek dsh-superagent?
It is a GitHub agent that waits to be asked. When an allowed person addresses it, it opens a work item, called a Ticket. Each Ticket runs in its own session and directory. Results return to the original conversation, often as a pull request. A local web panel lists only what needs a person.
The README page states the core rule plainly: take as little human attention as possible, and give as much value as possible. The philosophy doc applies it to behavior. The agent confirms once, stays quiet while working, and writes again only with a result, a question or a failure.
Why did DeepSeek build a Super-agent?
DeepSeek team argues that mainstream agents treat 1 session as the basic unit. According to the report, as reported by ifeng, a person can actively follow only 3 to 5 sessions at once. Human attention, not model capability, becomes the bottleneck for multitasking.
How does the Router-Worker architecture work?
The research team describes 2 layers. A long-running Router handles interaction and dispatch. Isolated Worker sessions do the actual work.
In the prototype, that flow is concrete, per the domain model:
- Source → Trigger: GitHub or Feishu produces events.
- Eval: decides whether a batch of Triggers asks for new work.
- Ticket: a unit of authorized work, with an immutable Mandate stating why it may proceed.
- Subscription: later comments on a pull request reach the right Worker without a fresh mention.
The main session runs the dsh-superagent-router preset with a smaller tool set. Each Worker runs dsh-superagent-worker. Shell commands move to the background after 5 seconds, so Workers keep receiving messages, per the execution doc.
Proactive work is gated. The authorization doc defines 3 accept modes: off, observe (default) and auto. In observe, the agent only records what it would have accepted.
The research team also lists ideas beyond the prototype: an external Watchdog, storage and compute separation, and an asynchronous ReAct loop.
How does it perform?
DeepSeek tested with simulated group chat and GitHub CI streams. Each run had about 300 messages, of which about 20 were real work requests. Each scheme ran 5 times, per ifeng’s account of the report.
- Super-agent: p50 of 12.4 minutes, p95 of 15.7 minutes.
- Single session plus subagents, neutral prompt: p95 of 29.5 minutes. No p50 was given.
- Why: under the neutral prompt, the baseline called a subagent 0 times in 5 runs.
- Router load: as arrivals rose from about 1 to 40 per minute, batch size grew from 1.0 to 2.8. Per-request latency rose from 2.5 to 3.9 seconds.
An ablation removed direct event pass-through. Router decisions rose about 30%, and cache-read tokens rose about 37%, from 23.23M to 31.77M. All figures are self-reported and come from secondary reports of the report.
How does dsh-superagent compare with other GitHub coding agents?
Key Takeaways
- DeepSeek released dsh-superagent, an MIT-licensed proactive coding agent prototype.
- A Router dispatches work; isolated Worker sessions execute each Ticket.
- Simulated p95 task time: 15.7 minutes vs 29.5 minutes for a single session.
- Proactive work stays in observe mode until a team switches it on.
- It runs with full OS-user access, so isolate it before use.
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Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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