--- title: "How Slack Manages Context in Long-running Multi-agent Systems" source_name: "InfoQ" original_url: "https://www.infoq.com/news/2026/04/slack-agent-context-management/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global" canonical_url: "https://www.traeai.com/articles/e5429d7b-1050-48b5-91fb-ade73d4c0e52" content_type: "article" language: "英文" score: 7.5 tags: ["Slack","多智能体系统","架构","事件溯源"] published_at: "2026-04-28T21:00:00+00:00" created_at: "2026-04-28T23:16:55.442833+00:00" --- # How Slack Manages Context in Long-running Multi-agent Systems Canonical URL: https://www.traeai.com/articles/e5429d7b-1050-48b5-91fb-ade73d4c0e52 Original source: https://www.infoq.com/news/2026/04/slack-agent-context-management/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global ## Summary Slack分享了其在长时间运行的多智能体系统中管理上下文的设计与实践经验，重点探讨了架构挑战和解决方案。 ## Key Takeaways - Slack采用分层上下文管理策略以支持复杂多智能体交互。 - 通过事件溯源和状态快照提升系统的可扩展性和恢复能力。 - 设计强调模块化和隔离性，确保长期运行中的稳定性。 ## Content Title: How Slack Manages Context in Long-running Multi-agent Systems URL Source: http://www.infoq.com/news/2026/04/slack-agent-context-management/ Published Time: 2026-04-28T21:00:00+0000 Markdown Content: # How Slack Manages Context in Long-running Multi-agent Systems - InfoQ [BT](http://www.infoq.com/int/bt/ "bt") ## InfoQ Software Architects' Newsletter A monthly overview of things you need to know as an architect or aspiring architect. 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Peer conversations that change how you think.](https://qconsf.com/?utm_source=infoq&utm_medium=referral&utm_campaign=infoqyellowbox_qsf26) # How Slack Manages Context in Long-running Multi-agent Systems Apr 28, 2026 2 min read by * [![Image 11: Author photo](https://cdn.infoq.com/statics_s1_20260421232814/images/profiles/NovciOoQOAYWqYqRQBFo97SuMm0xbUiC.jpg)](http://www.infoq.com/profile/Sergio-De-Simone/)[Sergio De Simone](http://www.infoq.com/profile/Sergio-De-Simone/) Follow #### Write for InfoQ **Feed your curiosity.**Help 550k+ global senior developers each month stay ahead.[Get in touch](https://www.infoq.com/write-for-infoq/) Log in to listen to this article Audio ready to play Your browser does not support the audio element. 0:00 0:00 Normal 1.25x 1.5x Like * [Reading list](http://www.infoq.com/showbookmarks.action) To sustain productivity in long-running agent systems, Slack engineers moved away from accumulating chat logs and started [using structured memory, validation, and distilled truth](https://slack.engineering/managing-context-in-long-run-agentic-applications/) to maintain coherence and accuracy of long-running agent systems. While short LLM sessions do not usually require explicit context management, this becomes essential in long-running sessions to ensure coherence, as the growth of the message history makes it impractical to include the full context with each request: > Agent frameworks solve the state management problem for users by accumulating message history between API calls. This fills the agent’s context window, which provides a hard limit on how much information the agent can handle. Even approaching an agent’s context window limit can degrade the quality of responses. As Slack staff software engineer Dominic Marks explains, one of Slack's multi-agent applications can span over hundreds of requests and generate megabytes of output. To manage this complexity, they followed an approach based on three complementary context channels: a director's journal, storing the director's structured working memory; a critic's review, storing an annotated findings report with credibility scores; and a critic's timeline, storing chronological findings with credibility scores. ![Image 12](https://imgopt.infoq.com/fit-in/3000x4000/filters:quality(85)/filters:no_upscale()/news/2026/04/slack-agent-context-management/en/resources/1slack-image-1777407895831.jpg) Slack's approach follows a [_coordinator/dispatcher_ multi-agent design](https://www.infoq.com/news/2026/01/multi-agent-design-patterns/), where a central the coordinator acts as a decision maker, receiving requests and dispatching them to specialized agents further down the line, namely experts and critics. Critics evaluate experts's work, as a portion of their findings "could either be invented or grossly misinterpret the data". They receive summary reports from experts and assess the evidence they contain. This evaluation is the basis for the creation of a scoring system used to identify findings corroborated by multiple sources. The director's journal includes findings, observations, decisions, questions, and hypothesis, and "provides the common narrative that keeps other agents on track". The critic's review acts as a truth filter using evidence inspection tools to build a credibility-weighted list of findings. To reduce the risk of hallucinations, critics are narrowly instructed to "only make a judgement on the submitted findings". Finally, the critic's timeline builds a coherent narrative from the director's journal, the latest critic's review, and the previous timeline, retaining only credible evidence, removing duplicates, and resolving any conflicts by preferring strongest sources. While Slack's approach is strictly tied to their system, it illustrates a broader principle: rather than passing all information at every step, they build structured summaries that agents can reliably build on. The three channels: > work together to maintain coherence across rounds, while preserving the benefits of specialized agent roles. The Director can make informed strategic decisions. Experts can build on previous understanding. The Critic can objectively evaluate findings. This approach, Marks says, has proven effective in addressing the limitations of complex, long-running agentic applications. For full details and extensive examples, refer to the original article. ## About the Author [![Image 13](https://cdn.infoq.com/statics_s1_20260421232814/images/profiles/NovciOoQOAYWqYqRQBFo97SuMm0xbUiC.jpg)](http://www.infoq.com/profile/Sergio-De-Simone/) #### **Sergio De Simone** **Sergio De Simone** is a software engineer. Sergio has been working as a software engineer for over twenty five years across a range of different projects and companies, including such different work environments as Siemens, HP, and small startups. For the last 10+ years, his focus has been on development for mobile platforms and related technologies. He is currently working for BigML, Inc., where he leads iOS and macOS development. 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