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Rohan Paul@rohanpaul_ai
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2026-08-09 15:38· 21分钟前
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微软对 13.5M 次 GitHub Copilot 会话的生产轨迹分析显示，87% 的 LLM 调用来自智能体自身而非用户。KV 缓存命中率在单轮内从首次调用的约 45% 升至第三次起的 92–94%，模型切换时骤降至 8%。论文提出基于轮次和会话级特征的轻量预测器，可捕获 86–90% 的总空闲时间，表明编码智能体基础设施应按轮次调度工作流状态，而非请求级策略。

New Microsoft Paper on GitHub Copilot's production traces show why coding agents should not be served like chat requests.

In 13.5M GitHub Copilot sessions, 87% of LLM calls came from the agent itself rather than a user.

A user prompt can fan out into an autonomous chain of model calls, tool actions, retries, and growing context, making the turn or session a more useful scheduling unit than an isolated request.

That structure is especially visible in the KV cache.

It shows that KV cache is not really a request-level resource; its value depends on where the agent is in the workflow.

Within a turn, average cache hit rate rises from about 45% on the first LLM call to 92-94% from the third call onward.

At a same-model turn boundary it falls to 55%, while a model switch pushes it down to 8%.

Median KV-cache idle time is 1.2 seconds within a turn versus 172 seconds across turns, while container idle time jumps from 5.8 to 243 seconds.

Using turn- and session-level features, the paper's lightweight predictor captures 86-90% of total idle time, giving the serving stack a signal for cache offloading or container reclamation.

The implication is straightforward: coding-agent infrastructure should schedule workflow state across turns, because request-level policies discard some of the strongest signals in the workload.

  • arxiv. org/abs/2608.00101

Title: "Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale"

智能体arXivMicrosoft论文/研究

来源：Rohan Paul · x.com

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Rohan Paul · @rohanpaul_ai · X·2026-08-09 15:38·21分钟前
AI 导读

微软对 13.5M 次 GitHub Copilot 会话的生产轨迹分析显示，87% 的 LLM 调用来自智能体自身而非用户。KV 缓存命中率在单轮内从首次调用的约 45% 升至第三次起的 92–94%，模型切换时骤降至 8%。论文提出基于轮次和会话级特征的轻量预测器，可捕获 86–90% 的总空闲时间，表明编码智能体基础设施应按轮次调度工作流状态，而非请求级策略。

New Microsoft Paper on GitHub Copilot's production traces show why coding agents should not be served like chat requests.

In 13.5M GitHub Copilot sessions, 87% of LLM calls came from the agent itself rather than a user.

A user prompt can fan out into an autonomous chain of model calls, tool actions, retries, and growing context, making the turn or session a more useful scheduling unit than an isolated request.

That structure is especially visible in the KV cache.

It shows that KV cache is not really a request-level resource; its value depends on where the agent is in the workflow.

Within a turn, average cache hit rate rises from about 45% on the first LLM call to 92-94% from the third call onward.

At a same-model turn boundary it falls to 55%, while a model switch pushes it down to 8%.

Median KV-cache idle time is 1.2 seconds within a turn versus 172 seconds across turns, while container idle time jumps from 5.8 to 243 seconds.

Using turn- and session-level features, the paper's lightweight predictor captures 86-90% of total idle time, giving the serving stack a signal for cache offloading or container reclamation.

The implication is straightforward: coding-agent infrastructure should schedule workflow state across turns, because request-level policies discard some of the strongest signals in the workload.

  • arxiv. org/abs/2608.00101

Title: "Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale"

来源：Rohan Paul· x.com

智能体arXivMicrosoft论文/研究部署/工程