Agentic Scrum is not Scrum with bots in the stand-up

Those distinctions are worth keeping. Scrum separates activity from a usable Increment, requires visibility before inspection, and assigns accountability to people who can own decisions and their consequences. An AI agent may perform development work; the accountability stays where it was.

What follows preserves the Guide's sequence: a Done Increment exists before the Sprint Review, and the Review inspects the outcome and adapts what comes next. Release may happen before or after that event. The Review is not turned into a release gate or an acceptance gate.

Agentic work becomes hard to govern when done, verified, accepted and released are used interchangeably. Six states, each answering a different question:

• Execution complete — the assigned producer has created the required artifacts.
• Evidence-qualified — the result is tested against the acceptance criteria named in the assignment. Results that fail go back.
• Verified, or Done in Scrum — the result satisfies the defined technical and procedural criteria.
• Authorized — the next consequential action is permitted: a release, an external communication, a policy change.
• Released or put into use.
• Outcome measured — whether the intended business or user outcome actually appeared.

The rights stay separate too. An AI agent or an automated control may verify defined criteria. A deterministic gate may qualify a predefined low-risk transition under a policy people approved in advance. A decision-right holder accepts residual risk and authorizes a consequential action. The person or institution that delegated those rights remains accountable. Automation does not acquire accountability by applying a rule — it executes previously delegated authority inside a defined envelope.

Human and AI agent work run at different speeds. The human loop moves from goal to priority to judgment to accountable decision, and may take hours, days or a whole Sprint, because its context is social and organizational. Inside that loop, AI agent cycles move from execution to checking to repair in seconds or minutes, several of them in parallel before human attention is needed.

Forcing both clocks into the same ceremony either destroys the speed advantage or weakens the control. People should not attend a meeting for every AI agent pass, and AI agents should not run far beyond the state that people can inspect and authorize. The alternative is nested: people set autonomy boundaries, verification requirements, escalation triggers and review capacity; AI agents execute inside them; human attention goes to exceptions, conflicting evidence, changed risk, and decisions that exceed delegated policy.

• Planning still asks why the Sprint is valuable and what can be done. It adds the design of parallelism and verification: isolated workspaces, dependencies, integration order, independent checks, escalation paths, conflict ownership, and the maximum review load the team can absorb.
• The Daily stays an inspection of progress toward the Sprint Goal, but routine AI agent status is collected automatically. Human discussion concentrates on failed checks, contradictory outputs, blocked dependencies, changed external facts, approaching permission or cost limits, and decisions that need judgment.
• The Sprint Review stays a working session for inspecting the outcome and adapting the backlog. The agentic addition is a clearer evidence trail: which Increment is verified, what was released, what outcome has been observed, and what uncertainty remains.
• The Retrospective expands from social learning to system learning. A recurring failure becomes a corrected context source, a changed task template, a new test, a tighter permission, a different routing rule or a revised escalation threshold — not a story people and future AI agents are expected to remember.

The arithmetic under Planning is the part teams skip. If AI agents can generate one hundred changes while the team can verify ten, the plan has produced a queue and called it capacity.

Existing standards already carry parts of the same architecture. SLSA formalizes provenance and artifact verification for software supply chains. OpenTelemetry's GenAI conventions can represent AI agent, model and tool execution in one trace. The Model Context Protocol recommends that a person be able to inspect and deny sampling requests. None is a management framework on its own, and together they make the point that evidence and control can be engineered rather than reconstructed from conversational memory.

This completes a correction the series has been making in four steps. Capability is not self-management. Communication is not coordination. Activity is not productivity. And production is not a verified outcome. Each separates a thing that is easy to observe from a thing that actually decides whether the work was worth doing.

8Hats Lab's working answer is the human–AI team: people and AI agents inside one outcome boundary, with different interfaces and different rights. People keep accountable judgment, AI agents provide execution and search, automated controls verify defined conditions, and policy governs which transitions may happen without another decision. The task definition that moves through it holds three synchronized views rather than one prose ticket.

It is an 8Hats lifecycle proposal, and it has not been tested against alternatives. The site says the same thing on its research pages, and this page does not say it more confidently than that.