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What Does a Scrum Master Do in a Typical Day
What a Scrum Master does each day, from the daily Scrum to planning, review, and retrospective, plus the checklists they run with the Product Owner and team.
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A Scrum Master spends a typical day running the Scrum events and clearing whatever blocks the team. The 2020 Scrum Guide timeboxes the Daily Scrum at 15 minutes and leaves its structure to the Developers, so the Scrum Master facilitates the event instead of chairing it. This guide covers the Scrum Master activities in the daily Scrum, the planning meeting and the review meeting, how the day changes when the team uses AI agents, and the checklists they run with the Product Owner and the development team.
Key Takeaways
- A Scrum Master's day is organised around the Scrum events, which are the daily Scrum, sprint planning, the sprint review, and the retrospective, each with its own set of checks.
- During the daily Scrum, a Scrum Master protects the timebox, keeps every team member focused on the Sprint Goal, and moves technical problem-solving into a separate discussion.
- During sprint planning, a Scrum Master works with the Product Owner so each candidate story carries clear acceptance criteria and a Definition of Done, and helps the team avoid both over-commitment and under-commitment.
- During the sprint review and the retrospective, a Scrum Master makes sure the team can present completed work with metrics and impediments, and that every member can speak without the session turning into blame.
- A Scrum Master also runs standing checklists on the Product Owner and the development team, covering backlog visibility, prioritisation, velocity, technical debt, and self-organisation.
- When a team uses AI agents, the Scrum Guide Expansion Pack keeps humans accountable for AI-generated work and states that the Scrum Master must be a human.
How does a Scrum Master's day change when the team uses AI agents?
The events stay the same and the accountability stays human. A Scrum Master adds one job to each event, which is checking that work produced by AI is still reviewed and owned by a person. In January 2026 the Scrum Guide Expansion Pack published a companion section called AI and Scrum, written by Ralph Jocham and Jeff Sutherland. It proposes a team working agreement stating that AI tools and autonomous agents may generate or perform work such as code, tests, documentation, analysis, research, planning or recommendations, but humans remain accountable. The same agreement asks teams to monitor their AI agents, set clear boundaries on what the agents may do, and keep a fast manual stop or override in place.
That agreement turns into concrete work inside the events listed above. For Sprint Planning the guidance suggests discussing which tasks the AI will be used for and setting aside time to validate AI outputs, so the estimate covers review and not only generation. For the Sprint Retrospective it suggests the Scrum Master prompt the team to inspect how AI helped or hindered during the Sprint. The Definition of Done carries the same load, because the guidance states that AI outputs must be reviewed and tested, and that every piece of AI-generated code must be reviewed with the same rigor as if a teammate wrote it.
Two patterns are worth watching in the daily work. The first is uneven adoption, where one team member becomes the AI guru and the others disengage. The second is skill atrophy, which some teams counter by scheduling occasional coding exercises without AI. The Scrum Guide Expansion Pack also draws a line through the accountabilities. A Product Developer may be human or automated, and at least one Product Developer should be human, but the Scrum Master must be human. Facilitation, coaching and impediment removal stay a person's job.
Key Takeaway: When a team uses AI agents, a Scrum Master sets aside planning time to validate AI output, keeps AI-generated code reviewed as rigorously as a teammate's, and watches for uneven adoption and skill atrophy in the team.
Key Takeaway: The Scrum Master checklists test whether the Product Owner keeps a visible, prioritised backlog and stays available to the team, and whether the development team manages technical debt, tracks honest velocity, and moves toward self-organisation.
Author
David Tzemach is a software quality and engineering leader with 19+ years of experience in software testing, quality assurance, and large-scale R&D operations. He specializes in building QA organizations from scratch, defining quality frameworks, and implementing agile and shift-left testing practices across enterprise environments. David has served as Head of QA and QA Architect, authored multiple books on agile quality and testing, and actively contributes to the testing community through his QualityBreach platform and publications.
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