Purpose

This document describes what an Agentic Sprint looks like across one working week. The Specification defines the lifecycle and its three human gates. The Operating Model describes the roles and the ceremonies. Neither says what a lead engineer, a QA engineer and a pair of coding agents actually do on a Tuesday. This document does.

It is written for a team that has read the methodology and wants to start running it next Monday. It uses Claude Code and Codex as the example agents because they are the tools most teams reach for first. Nothing in the cadence depends on either product. Any agent that can work in an isolated workspace, run the repository's checks and leave evidence behind can fill the same seat.

This is an informative document. It adds no requirements to D1, and its lowercase recommendations describe one way to arrange the week. A team that runs a two-week cadence, or that places the gates on different days, can still conform to the specification.

The one change that matters

A conventional sprint fits a set of human implementation tasks into a timebox. The week is spent producing the work.

In an Agentic Sprint the humans do not spend the week producing the work. They select it, authorise it, validate it and release it. The agents consume the execution capacity, continuously, from Monday afternoon to Friday morning. That inverts where the week's effort goes:

Table 8
Conventional sprintAgentic Sprint
Who implementsEngineers, for most of the weekMaker agents, inside an authorised boundary
What limits the sprintEngineering hoursHuman validation and decision capacity
Where humans concentrateWriting and integrating codeMonday decisions, Thursday validation, Friday release and learning
What a green pipeline meansThe change is probably ready for reviewThe change is a Candidate. Acceptance is still a human decision
How the team learnsA retrospectiveA learning review that can change next week's agent context

The rest of this document follows from that inversion. Agent compute is elastic and human judgement is scarce, so the cadence is designed to protect human judgement. Decisions are batched into short ceremonies, agents run unattended between them, and a hard weekly boundary stops the queue of generated work from outgrowing the people who have to validate it.

The week at a glance

Conceptual model

The 5-Day Agentic Sprint Cadence

The week runs left to right through five lanes. On Monday a ready backlog is narrowed by human sprint selection, planning agents write a Build Plan for every selected item in parallel, and Human Gate 1, Build Plan Approval, authorises each plan. On Tuesday maker agents such as Claude Code or Codex implement each approved plan in an isolated worktree or sandbox. A boundary exception, such as an unexpected migration or a security concern, pauses the item and escalates to human execution triage, which returns a decision to the maker. On Wednesday the execution cutoff stops new implementation. Checker agents verify each change, a failed check returns as maker remediation, and a passing check produces a candidate with evidence. On Thursday Human Gate 2, Engineering Implementation Acceptance, accepts the candidate and independent QA validates product behaviour. A QA return goes back to remediation. On Friday the most impacted business stakeholder validates the business outcome of each accepted candidate, then Human Gate 3, Release Authority, decides ship, hold or reject, and the learning review examines every human correction. A reusable correction becomes a versioned context change that informs next Monday's planning agents. No agent approves its own work, and no maker has a path to release.

Monday: decideSelect the week's backlog, let the agents plan it, approve the plans.
  1. Ready backlogItems that meet the Definition of Ready, with owners and risk tiers.
  2. Sprint selectionProduct owner, lead engineer and QA lock the Agentic Sprint Backlog to what humans can review.
  3. Planning agentsWrite a Build Plan for every selected item in parallel, changing no code.
  4. Build Plan ApprovalHuman Gate 1. Each plan is approved, revised or rejected in the Monday plan review.
Tuesday: buildMaker agents run unattended. Humans are available for exceptions and otherwise do their normal work.
  1. Maker agentsClaude Code or Codex implement one approved plan each in an isolated worktree or sandbox.
  2. RiskBoundary exceptionA blocked dependency, deviation, migration, security concern or repeated failure pauses the item.
  3. Execution triageThe lead engineer decides: continue, narrow or stop. Nobody watches agents type.
Wednesday: proveNo new implementation after the cutoff. Checker agents verify, Maker agents remediate. Humans are available for escalation.
  1. Execution cutoffNew planned implementation does not start after Wednesday. Remediation may continue.
  2. Checker agentsA separate agent, often a different tool from the Maker, verifies the change against the plan and the criteria.
  3. Maker remediationFindings return as bounded goals and the checker runs again on the result.
  4. Candidate with evidenceDiff, plan versus actual, tests, checker and security evidence, ready for a person.
Thursday: validateHuman validation. Engineers accept the technical outcome, QA validates the behaviour.
  1. Engineering Implementation AcceptanceHuman Gate 2. The lead engineer asks whether it built what was approved.
  2. Independent QAThe QA engineer validates acceptance criteria and journeys against product intent.
  3. RiskQA returnA failed criterion goes back to the maker with evidence and usually misses this week's release.
Friday: ship and learnThe business stakeholder validates the outcome, release authority decides, and the learning review turns corrections into context.
  1. Stakeholder validationThe most impacted business stakeholder confirms the outcome is the one the business asked for. Full UAT can follow.
  2. Release AuthorityHuman Gate 3. Ship, hold or reject, with the reason recorded.
  3. ReleaseThe approved change moves through protected release controls.
  4. Learning reviewAsks what humans had to correct, and whether each correction is reusable.
  5. Versioned context changeA reviewed, tested update to architecture guidance, agent instructions, a skill or a checker rule.
Edges
  • Planning agents leads to Build Plan Approvalsubmits Build PlansGated
  • Build Plan Approval leads to Maker agentsauthorises implementationGated
  • Maker agents leads to Boundary exceptionmeets an unauthorised boundaryFailure
  • Execution triage leads to Maker agentsreturns a decision or narrows scopeFeedback
  • Execution cutoff leads to Checker agentsopens finished work for challengeGated
  • Checker agents leads to Maker remediationrecords failed checkFailure
  • Maker remediation leads to Checker agentsresubmits for re-checkFeedback
  • Candidate with evidence leads to Engineering Implementation Acceptancesupports engineering acceptanceGated
  • Engineering Implementation Acceptance leads to Independent QApasses to independent QAGated
  • Independent QA leads to QA returnrecords failed criterionFailure
  • QA return leads to Maker remediationreturns with evidenceFeedback
  • Independent QA leads to Stakeholder validationreports product outcomeGated
  • Stakeholder validation leads to Release Authorityconfirms business outcomeGated
  • Release Authority leads to Releaseauthorises releaseGated
  • Learning review leads to Versioned context changeproposes a reusable lessonGated
  • Versioned context change leads to Planning agentsinforms next Monday's plansFeedback
Legend
  • Human ceremonySelection, triage, acceptance, QA, release and learning are people's work.
  • Agent workPlanning, making and checking run unattended between the ceremonies.
  • Gate or cutoffA boundary the week does not cross without a decision.
  • Evidence and contextWhat the humans read on Thursday and what the agents load next Monday.
  • Return pathAn exception, failed check or QA return goes back with its evidence.
Humans decide on Monday, Thursday and Friday. Agents build and prove in between. A Wednesday cutoff keeps the two in balance.

Mapped onto five days:

Table 13
DayAgentsHumansHuman GateExit condition
MondayAnalyse the selected items and write Build PlansSelect scope, clarify, approve or reject plansHuman Gate 1Agentic Sprint Backlog authorised
TuesdayMaker agents build in isolated workspacesAvailable for exceptions onlyPull requests with evidence
WednesdayChecker agents verify, Maker agents remediateAvailable for escalation. Enforce the cutoffExecution cutoffEvery item is a Candidate or Not ready
ThursdayTargeted fixes and re-checksValidate the Candidates: engineering, QA, productHuman Gate 2Accepted items
FridayRelease evidence, summaries, proposed contextStakeholder validation, release decision, learning reviewHuman Gate 3Sprint closed, context versioned

Or, more simply: Monday decides, Tuesday builds, Wednesday proves, Thursday validates, Friday ships and learns.

The three human decision points are the ones the specification already defines: Build Plan Approval on Monday, Engineering Implementation Acceptance on Thursday and Release Authority on Friday. The cadence does not add a gate. It gives each gate a day.

Allocated, not occupied

An Agentic Sprint runs for five days. The agents are continuously progressing the work through the pipeline. Humans intervene only where judgement, approval, validation or authority is required. People are allocated to the sprint without being consumed by it.

The five-day cadence is a five-day workflow for agents. It is not five days of full-time human allocation. The people named on a sprint are accountable for its gates and available for its escalations. Between those points they keep their capacity for normal engineering, product, support, meetings and other projects.

Conceptual model

Allocated, not occupied: who is engaged on which day

A week of five days with an agent lane and a human lane per day. Monday is human decision concentration: agents are high, analysing and planning; the product owner is high, selecting scope and clarifying intent; the lead engineer is medium, reviewing and approving plans; QA is low, reviewing test strategy. Human Gate 1 falls on Monday. Tuesday is agent-heavy execution: agents are high as Maker agents build in isolation; every human lane is low, available for escalation only. Wednesday is agent-heavy verification: agents are high as Checker agents verify and Maker agents remediate; every human lane is low, with the lead engineer enforcing the execution cutoff. Thursday is human validation concentration: agents are medium for targeted fixes and re-checks; the lead engineer and QA are high, validating candidates; the product owner is medium, confirming intent and edge cases. Human Gate 2 falls on Thursday. Friday is human release and learning: agents are medium, preparing release evidence and summaries; every human lane is medium for the stakeholder validation, the release decision and the learning review. Human Gate 3 falls on Friday. A high level is a concentrated block of the day, a medium level is a bounded task, and a low level is availability for an escalation, a question or a spot-check. Between those, people do their normal work. Humans are allocated to the sprint, not occupied by it.

  1. MondayHuman decision concentrationHuman Gate 1
    Agent lane
    • Agents HighAnalyse items, write Build Plans, flag ambiguity
    Human lane
    • Product owner HighSelect scope, clarify, approve intent
    • Lead engineer MediumReview and approve plans
    • QA engineer LowReview test strategy
  2. TuesdayAgent-heavy execution
    Agent lane
    • Agents HighMaker agents build in isolation
    Human lane
    • Product owner LowAnswer intent questions
    • Lead engineer LowEscalations only
    • QA engineer LowPrepare cases from the plans
  3. WednesdayAgent-heavy verificationExecution cutoff
    Agent lane
    • Agents HighChecker agents verify, Maker agents remediate
    Human lane
    • Product owner LowEscalations only
    • Lead engineer LowEscalations, enforce the cutoff
    • QA engineer LowFinish cases, spot-check evidence
  4. ThursdayHuman validation concentrationHuman Gate 2
    Agent lane
    • Agents MediumTargeted fixes and re-checks
    Human lane
    • Product owner MediumConfirm intent and edge cases
    • Lead engineer HighValidate candidates
    • QA engineer HighValidate behaviour
  5. FridayHuman release and learningHuman Gate 3
    Agent lane
    • Agents MediumRelease evidence, summaries, proposed context
    Human lane
    • Product owner MediumStakeholder validation, next week's selection
    • Lead engineer MediumRelease decision, learning review
    • QA engineer MediumStakeholder session, learning review

Three bars: a concentrated block of the day. Two: a bounded task. One: available for an escalation, a question or a spot-check. Between the bars, people do their normal work.

Legend
  • Agent laneContinuous execution: planning, making, verifying, remediating.
  • Human laneIntervention points: decide, escalate, validate, release, learn.
  • Human GateA decision that has to be made that day by a named person.
Agents run continuously through the pipeline. People are engaged at the gates and available for escalation in between. Human attention peaks on Monday and Thursday, and the rest of each person's week is their normal work. Humans are allocated, not occupied.

Five properties follow, and the rest of the page keeps returning to them:

  • Asynchronous by design. Agents do not wait for a person to be watching. A Maker agent that meets something outside its authority pauses and escalates; a person answers when they are next free, and the agent resumes. Nobody pair-programs with an agent or supervises it typing.
  • Jump in and out. An engineer allocated to the sprint approves plans on Monday, answers an escalation on Tuesday if one arrives, does little or nothing on Wednesday, spends real time validating on Thursday and joins the release decision on Friday. The rest of the week is their normal work.
  • Human attention goes where human judgement creates the most value. Interpreting intent, approving an approach, judging whether a Candidate is right, safe and acceptable, and deciding to ship. Not watching tests run.
  • Human validation capacity is the constraint. Sprint capacity is set by how much agent-produced work the people can deeply validate on Thursday, not by how much the agents can generate.
  • Accountability is dedicated. Utilisation is not. Every sprint has named humans for intent, technical approval, validation and release. None of them is booked for five full days.

The people and the agents

A team running this cadence needs the roles in the Operating Model. In a small team they map onto people like this, with the days on which each is actually engaged:

  • Product owner. Owns the meaning of each requirement and its acceptance criteria. Engaged on Monday morning for selection and clarification, reachable during the week for intent questions, present on Thursday for edge cases and on Friday for the stakeholder validation.
  • Lead engineer. Approves Build Plans on Monday afternoon, answers escalations on Tuesday and Wednesday, holds Engineering Implementation Acceptance on Thursday and joins the release decision on Friday. Usually also the context steward.
  • QA engineer. Reviews the test strategy in each Build Plan on Monday, prepares independent cases during the week, validates behaviour on Thursday and runs the stakeholder session on Friday.
  • Release authority. Decides ship, hold or reject on Friday morning. Often the lead engineer or an engineering manager. For high-risk work it should be a different person from the plan approver.
  • Security owner. Reviews any item whose Build Plan touches identity, permissions, secrets, sensitive data or an externally reachable interface. Consulted on Monday, escalated to on Wednesday.
  • Business stakeholder. The person most affected by the outcome: the operations lead whose queue changes, the finance owner whose report moves, the support manager whose customers see it. Fifteen minutes on Friday morning to confirm that the accepted change is the outcome the business asked for. Not a tester. A witness that the business result is real.

The agents are role holders too, and the tools can be assigned to them in more than one way:

  • Planning agent. Reads the work item, the repository and the versioned context, and writes a Build Plan. Claude Code in plan mode, or a Codex task instructed to produce a plan and change no code, both work.
  • Maker agent. Implements one approved plan in an isolated worktree or cloud sandbox. Claude Code running against a git worktree, or a Codex cloud task with its own container, are typical.
  • Checker agent. Verifies the Maker agent's change against the approved plan, the acceptance criteria and the repository's rules. It does not share the Maker's session or memory. A practical arrangement is to check with a different tool from the one that built: Codex verifying a Claude Code change, or Claude Code verifying a Codex change. Different models make different mistakes, and that difference is part of the independence.
  • Coordinator. The orchestration that distributes approved plans, tracks state, pauses at gates and routes escalations to a person. It can be an off-the-shelf orchestrator, the optional Autonomous Loop profile, or a lead engineer with a board and a script. What matters is that it cannot approve or release.

The Maker agent is never its own Checker. That rule from the Quality and Verification Model is the reason the week has a Wednesday.

Two words are kept apart throughout. Verification is what agents and deterministic checks do before Thursday: did we build what we planned, and can we prove it? Validation is what humans do on Thursday and Friday: is this actually right, safe and acceptable? A Candidate is verified. Only a person can validate it.

Monday: humans define and approve

Human engagement is concentrated around planning and approval. Once the decisions are made, people return to their other work.

Agents. Analyse the selected backlog items. Read the requirements and the relevant repository and project context. Generate Build Plans. Identify ambiguity, dependencies and risks. Propose test strategies and implementation approaches.

Humans. Select the sprint scope by how much work they can realistically validate on Thursday. Clarify requirements where the agents flag ambiguity. Review the generated plans. Challenge architecture or implementation decisions where necessary. Approve, reject or request changes. Give Human Gate 1 approval.

Monday morning: select the week's Agentic Backlog

Do not bring the whole product backlog into the agentic system. Monday morning is a 30 to 45 minute Agentic Sprint Planning session that narrows the backlog three times.

Conceptual model

Narrowing the backlog on Monday morning

The product backlog of about eighty items is filtered first by the Definition of Ready, leaving about fifteen items ready for agentic execution, and then by the week's human review capacity, leaving an Agentic Sprint Backlog of six items, AS-101 to AS-106. Agent capacity could take all fifteen; the team's capacity to review, test and accept cannot. The numbers are an illustration, not a target.

  1. Product backlog: about 80Everything the product owner could ask for.
  2. Ready: about 15Each meets the Definition of Ready: intent, criteria, context, risk tier, owner.
  3. Sprint backlog: 6AS-101 to AS-106. What the lead engineer and QA engineer can accept this week.

The last filter is the one that matters. Agent capacity could take all fifteen. The team's capacity to review, test and accept cannot.

Three filters. The last one is human review capacity, not agent capacity, and it is the one that sets the sprint.

The important narrowing is the last one. There may be enough Claude Code and Codex capacity to generate forty pull requests in a week. That does not mean the team should generate them. If the lead engineer and the QA engineer can properly validate six changes on Thursday, then the sprint capacity is six.

Before an item enters the sprint it needs the entry conditions in the Definition of Ready: requirement, acceptance criteria, context, risk tier, testability, dependencies and a named human owner. A short card per item is enough for the planning session:

AS-104  Add scheduled payment cancellation
        Risk tier:          R2 (financial state)
        Repositories:       payments-api, web
        Acceptance:         5 criteria, each with a verification method
        Dependencies:       none
        Engineering owner:  lead engineer, Payments
        QA owner:           QA engineer, Payments
        Agent complexity:   medium

If the requirement is unclear, the item does not enter the sprint. It becomes a clarification or discovery task and goes back to the product owner. The cost of ambiguity is multiplied by execution speed, so Monday morning is the cheapest place to stop it.

Monday afternoon: agents plan, humans approve

The planning agents are released across the whole sprint backlog in parallel. Six items go to six planning agents at once, and six Build Plans come back, each following the Build Plan Standard: interpretation, scope, affected components, sequencing, data impact, security, test strategy, rollback, risks and a traceability table from every acceptance criterion to a verification condition. The Build Plan Template is the form.

Six plans take the agents minutes. Reading them takes the humans a 60 to 90 minute session: the lead engineer, the QA engineer and, for R2 or higher items, the security owner. They are not reviewing code. They are reviewing what the agents intend to build. Each plan gets one of three decisions:

  • Approve. The plan becomes the authorised implementation boundary and the item moves to Authorised.
  • Revise. The plan goes back to the planning agent with specific feedback. Revisions usually return within the hour and are re-read, not re-run from scratch.
  • Reject. The requirement or the approach is wrong. The item leaves the sprint and returns to discovery. Rejecting on Monday is cheap. Rejecting on Thursday is not.

That is Human Gate 1. The reviewer's attention goes to the assumptions most likely to cause an expensive or unsafe implementation: the interpretation of the requirement, the architecture, the data impact and the rollback path. Once a plan is approved, the item moves to Authorised, the agents get the rest of the week, and the reviewers get the rest of their day.

Tuesday: agents build, humans handle exceptions

For most participants, Tuesday has the lowest Agentic Sprint involvement of the week. The agent fleet executes. People continue their normal work.

Agents. Maker agents execute the approved plans in isolated branches, worktrees or sandboxes. They implement code and tests, collect implementation evidence, and work several items concurrently.

Humans. Available for exceptions. Answer genuine ambiguity that the existing context could not resolve. Make the business or product decisions an agent is not authorised to make. Unblock an agent where human authority is required. Prevent scope drift. Nobody pair-programs with an agent or watches it work.

Conceptual model

Tuesday: one Maker per item, one workspace per Maker

Human Gate 1 releases three approved plans, AS-101, AS-102 and AS-103. Each goes to its own Maker agent: Maker A running Claude Code, Maker B running Codex, Maker C running Claude Code. Each Maker works in its own isolated workspace, a git worktree for Claude Code and a cloud sandbox for Codex, on its own branch, with only the permissions its plan requires. Every workspace runs the repository's checks and produces an evidence bundle: the diff, the plan-versus-actual comparison, test output and the Checker's first findings. No Maker shares a working directory with another and no Maker holds production credentials.

Approved plansHuman Gate 1 has passed for each item.
  1. Build Plan ApprovalHuman Gate 1. Three approved plans: AS-101, AS-102, AS-103.
MakersOne agent per item, started by the coordinator.
  1. AS-101: Maker AClaude Code, running against a git worktree.
  2. AS-102: Maker BCodex, running as a cloud task in its own container.
  3. AS-103: Maker CClaude Code, running against a second git worktree.
WorkspacesSeparate working state, separate branch, task-scoped permissions.
  1. Git worktreeA separate working tree on the same clone, on its own branch.
  2. Cloud sandboxA fresh container per run, with the repository cloned into it.
  3. Git worktreeA separate working tree on the same clone, on its own branch.
Checks and evidenceEach workspace runs the repository's checks and hands back a bundle.
  1. Repository checksLint, build, unit, integration and contract tests, run inside each workspace.
  2. Evidence bundleDiff, plan versus actual, test output and the Checker's first findings, per item.
Edges
  • Build Plan Approval leads to AS-101: Maker Aauthorises AS-101Gated
  • Build Plan Approval leads to AS-102: Maker Bauthorises AS-102Gated
  • Build Plan Approval leads to AS-103: Maker Cauthorises AS-103Gated
Legend
  • Maker agentClaude Code or Codex, one approved plan each.
  • Isolated workspaceA git worktree or a cloud sandbox, never shared.
  • EvidenceWhat comes back for Wednesday's Checker and Thursday's humans.
  • AuthorisationOnly an approved plan starts a Maker.
Three items, three Makers, three isolated workspaces. Nothing is shared except the evidence they hand back, and none of them can reach production.

Git worktrees give Claude Code a separate working tree per item on a single clone. A Codex cloud task gets a fresh container per run. Either way, two Maker agents never share a working directory, and neither has production credentials. A Maker agent following the optional Autonomous Loop profile decomposes its plan into dependency-ordered goals with persistent state, so that a failed check becomes a recorded remediation goal rather than a retry from memory.

By the end of Tuesday, most items have a pull request open with tests passing and a first bundle of evidence: the diff, the plan-versus-actual comparison, test output and the Checker agent's first findings.

What reaches a human on Tuesday

A Maker agent escalates, and the coordinator pauses the item, when it meets something outside its authorised boundary:

  • a blocked dependency the plan did not anticipate;
  • a deviation from the approved architecture;
  • an unexpected database migration;
  • a security concern, such as a credential or a new trust boundary;
  • an acceptance criterion that turns out to be ambiguous once it meets the code;
  • repeated Checker agent failure on the same goal.

An escalation arrives in a standing channel and waits for the named person to be free. The lead engineer decides, the Maker agent continues, narrows or stops, and the engineer goes back to what they were doing. A lead engineer who spends Tuesday reading agent transcripts has been demoted to a supervisor of typing.

Wednesday: agents prove the work

Wednesday is not the main human review day. That is Thursday. Wednesday is machine-driven verification, and it answers one question: did we build what we planned, and can we prove it?

Agents. Checker agents inspect Maker output. They check the implementation against the approved Monday plan, run the automated tests, inspect acceptance-criteria coverage, run static analysis and the other deterministic controls, verify the expected repository changes and produce evidence. Failures go back to the Maker agent as targeted remediation goals, and the Checker agent re-checks the corrected implementation.

Humans. Handle exceptional escalations. Resolve the issues that require human judgement. Enforce the Wednesday cutoff. Spot-check evidence if they choose to. For most people this is another low-involvement day.

Conceptual model

Wednesday: the Maker, Checker, Maker loop

A goal from the approved plan goes to the Maker agent, which produces an implementation with unit, integration and contract tests. Deterministic checks run first. A Checker agent, often a different tool from the Maker, then verifies the change against the approved plan, the acceptance criteria and the repository's rules. If it passes, the result becomes a candidate with evidence, or the next goal starts. If it fails, the failure is recorded with evidence and returns to the Maker as a remediation goal; the Checker runs again on the remediated result. The Maker has no edge to approval.

MakeThe Maker takes one goal and produces an implementation with tests.
  1. GoalOne dependency-ordered goal from the approved plan, with a verification condition.
  2. Maker agentImplements the goal in its isolated workspace.
  3. Implementation and testsCode plus unit, integration and contract tests.
VerifyChecks run, then a Checker agent verifies the result.
  1. Deterministic checksLint, build, test suite, scans. Retained as evidence.
  2. Independent CheckerDoes it do what was approved? Does every criterion have a test? What would a hostile reviewer find?
OutcomePass moves forward. Fail returns with evidence.
  1. Candidate with evidenceOr the next goal, until the item is complete.
  2. RiskFailed checkRecorded with findings, evidence and the next hypothesis.
  3. Maker remediationThe findings become bounded goals. New implementation does not start after the cutoff; remediation may.
Edges
  • Independent Checker leads to Candidate with evidencepassesGated
  • Candidate with evidence leads to Goalselects the next goalFeedback
  • Independent Checker leads to Failed checkfailsFailure
  • Maker remediation leads to Independent Checkerresubmits for re-checkFeedback
Legend
  • MakerImplements a goal. Cannot pass its own work.
  • Checker agentA separate agent with a narrow question. Verification, not validation.
  • EvidenceTests, checks and the candidate bundle.
  • Failed checkReturns to the Maker with findings. Never silently retried.
The Maker never judges its own work. A failed check returns as a bounded remediation goal, and the Checker runs again on the result.

The same agent never reports that it wrote the change and its own tests passed. The methodology rejects that model: independent checks, deterministic controls and human validation are three separate challenge mechanisms, and Wednesday is when the first two do their work, so that Thursday's humans receive Candidates rather than drafts. The Checker agent has a narrow question. Given the approved plan, the acceptance criteria, the repository rules and the diff: does the implementation do what was approved, does every criterion have a test, and what would a hostile reviewer find?

The Wednesday execution cutoff

The cadence adds one rule of its own: new planned implementation does not start after Wednesday. Remediation can continue after the cutoff. New goals cannot.

Without a cutoff, cheap agent capacity produces exactly the failure the methodology warns about.

Conceptual model

Why the week needs a cutoff

The matrix compares agent generation capacity with human validation capacity across three columns. In one week, agents running in parallel could plausibly generate forty pull requests, while the lead engineer and QA engineer can review, test and accept about six outcomes. Agent capacity is limited by tokens, compute and the approved backlog, none of which the team feels on Thursday; human capacity is limited by attention and hours, because comprehension does not parallelise the way generation does. Exceeding agent capacity costs money; exceeding human capacity produces a queue nobody can read, superficial review or a missed release. The cutoff exists to keep the first inside the second.

Agent generation capacity and human validation capacity are not the same size. Sprint capacity is the smaller one.
DimensionVolume in one weekAn illustration, not a measurement.What limits itThe constraint the team actually feels.What exceeding it costsWhy the cutoff protects the smaller one.
Agent execution capacityWhat Claude Code and Codex can generate in a week.Forty pull requests is plausibleParallel Makers in isolated workspaces, running unattended from Tuesday.Tokens, compute and the approved backlogNone of which the team feels on Thursday.MoneyRecoverable, and visible on an invoice.
Human validation capacityWhat the lead engineer and QA engineer can deeply validate in a week.About six accepted outcomesOne reviewer's Thursday at 45 to 60 minutes each, and one QA afternoon.Attention and hoursComprehension does not parallelise the way generation does.A queue nobody can readSuperficial review or a missed release: the failure the methodology is built to avoid.
Legend
  • Agent capacityCheap, parallel, unattended.
  • Human validation capacityBounded by attention. This is the sprint capacity.
Agent generation capacity and human validation capacity are not the same size. Sprint capacity is the smaller one.

The backlog does not shrink. It moves from "waiting for development" to "waiting for someone to validate 37 AI-generated pull requests". The team's real constraint is human judgement, and the cutoff protects it by guaranteeing Thursday a bounded, finished set of Candidates.

Exit condition

By the end of Wednesday every sprint item is in one of two states, and there is no third:

  • Candidate. Agent verification passed and the evidence is sufficient for human validation.
  • Not ready. It does not enter Thursday. It carries its recorded state, evidence and last hypothesis into next week, or back to discovery.

Not endlessly "in progress". An item that is still in progress on Thursday morning is Not ready, and the reason is recorded for Friday's learning review.

Thursday: humans validate

Thursday is deliberately different from Wednesday. Wednesday was agent verification. Thursday is human validation, and it is where significant human attention is intentionally concentrated. It answers: is this actually right, safe and acceptable?

Agents. Targeted fixes for what validation finds, re-checks by the Checker agent, and updated evidence. Nothing new starts.

Humans. Engineering review, independent QA and, where required, product or domain validation. This is a Human Gate.

Conceptual model

Thursday: human validation, two decisions and the return path

For each candidate the lead engineer receives a review package in order: the approved Build Plan, the implementation diff, the plan-versus-actual comparison with every deviation named, the test evidence, the Checker agent's evidence and, where the risk tier requires it, the security evidence. Human Gate 2, Engineering Implementation Acceptance, asks one question: did it build what we approved? An accepted item goes to independent QA, which validates the acceptance criteria and user journeys against product intent. A QA pass makes the item a candidate for Friday's release. A QA failure returns with evidence: the Maker fixes, the Checker re-checks, engineering reviews again if the change is material, and QA runs again. An item that fails late on Thursday usually misses Friday's release.

Review packageWhat the lead engineer receives for each candidate, in reading order.
  1. Approved Build PlanWhat Monday authorised.
  2. Implementation diffThe change set on the item's branch.
  3. Plan versus actualEvery deviation from the approved plan, named.
  4. Test evidenceFresh results with the revision and environment under test.
  5. Checker evidenceThe Checker agent's findings and what was remediated.
  6. Security evidenceWhere the risk tier requires it.
DecisionsDid it build what we approved? Does it behave as the product intends?
  1. Engineering Implementation AcceptanceHuman Gate 2. The lead engineer asks whether it built what was approved.
  2. Independent QAThe QA engineer validates criteria and journeys against product intent.
  3. Candidate for FridayAccepted and verified. Waits for stakeholder validation and Release Authority.
Return pathA QA failure loops back with evidence and usually misses this week's release.
  1. RiskQA failureA criterion or journey did not hold. Recorded with evidence.
  2. Maker fixesA bounded remediation goal.
  3. Checker agent re-checksThe Checker agent runs again on the fix.
  4. Engineering review againOnly if the change is material.
Edges
  • Security evidence leads to Engineering Implementation Acceptancecompletes the packageGated
  • Engineering Implementation Acceptance leads to Independent QApasses to independent QAGated
  • Independent QA leads to Candidate for Fridayverifies product behaviourGated
  • Independent QA leads to QA failurerecords failed criterionFailure
  • Engineering review again leads to Independent QAQA againFeedback
Legend
  • Review packageRead in this order. Reduces search time, not judgement.
  • Human decisionEngineering acceptance, then independent QA.
  • Return pathA QA failure goes back to the Maker with evidence.
Verification happened on Wednesday. Thursday is validation: the lead engineer reads a package, not a transcript, and QA tests intent, not the Maker's tests.

Engineering review

For each Candidate the lead engineer receives a review package, not a stream of agent output: the approved plan, the diff, the plan-versus-actual comparison with every deviation named, the test evidence, the Checker agent's evidence and, where the risk tier requires it, the security evidence. The Review Evidence Template lists what it contains.

The engineer reviews implementation quality, the architecture and the important technical decisions, and the security and performance implications where they apply. The question is whether the implementation represents the intended solution, not whether it merely passes tests. The decision is Human Gate 2, Engineering Implementation Acceptance: accept the technical outcome and its residual risk, return the item to the Maker agent with specific findings, or reject it.

Six Candidates at 45 to 60 minutes each is most of a day for one lead engineer. That arithmetic, not the number of agents, is what set Monday's capacity.

Independent QA

Accepted items move to the QA engineer, who validates behaviour independently: the acceptance criteria, the user journeys, exploratory testing, scenarios the agents may not have anticipated, and actual usability. QA tests against product intent, not against whether the agents' own tests are green. The Quality and Verification Model places independent QA after engineering acceptance and before release authority, and the cadence keeps it there. The QA engineer has had since Monday's plan review to prepare cases, so Thursday afternoon is execution and exploration, not design.

Where an item needs it, the product owner or a domain expert validates the business intent, the edge cases and the expected outcomes alongside QA.

What happens when validation fails

Anything that fails validation loops back with evidence, along the return path in the figure: the Maker agent fixes, the Checker agent re-checks, engineering reviews again if the change is material, and QA runs again.

An item that fails on Thursday does not trigger a frantic attempt to squeeze it into Friday. It is returned to the backlog or the next sprint, and the reason is recorded. That is the correct outcome. The QA Evidence Template records what was tested and what was found.

Friday: humans control release and learning

Friday is a focused morning, not a full-day retrospective.

Agents. Prepare the release evidence. Produce summaries of each Candidate. Assist with deployment and release automation. Consolidate the week's corrections into proposed context changes.

Humans. Validate the business outcome with its stakeholder. Make the release decision: ship, hold or reject. Review where a human had to correct an agent. Decide which lessons become persistent agent context. Approve the resulting updates to versioned context, instructions, skills, guardrails or documentation.

Stakeholder validation

Before the release authority decides, the most impacted business stakeholder sees each accepted Candidate and validates the business outcome. Not the acceptance criteria: QA covered those on Thursday. The question is whether the result is the thing the business needed. Fifteen minutes with the person whose queue, report or customer the change affects catches the one error that survives every gate in the methodology: a correct implementation of the wrong outcome.

The product owner brings the stakeholder, the QA engineer runs the session on the accepted Candidate, and the answer is recorded with the evidence. Immediate validation of the business outcome is the bar for Friday morning. A full UAT cycle can follow the release where the risk tier or the organisation requires it, and its findings enter next week's backlog like any other correction. What does not happen is a release that nobody from the business has seen.

The release window

For every Candidate that reached it, the release authority checks the chain:

Table 95
Gate or checkWhat the release authority confirms
Human Gate 1The plan was approved, and the change is the one that was approved
Human Gate 2Engineering accepted the outcome and its residual risk
QABehaviour validated against intent, not against the Maker agent's tests
Business outcomeThe most impacted stakeholder has seen the Candidate and confirmed the outcome
SecurityChecks complete for the item's risk tier
CIGreen on the final revision
RollbackThe path stated in the plan is still usable
EvidenceRetained and navigable, so the decision can be reconstructed

Then Human Gate 3, Release Authority. The human decides ship, hold or reject. A hold is a release decision, not a failure. It says the change is accepted but the moment is wrong, for a reason that is recorded. The Maker agent never gains production authority by implication. The specification keeps release authority distinct from implementation, and a Friday morning release window is simply that distinction with a time on it.

The learning review

Friday's second session is short and has one question: what did a human have to correct this week that the agent should know next week?

Every correction from the week is examined. For example:

AS-104  Scheduled payment cancellation
        Checker agent failed twice on goal 3.
        Reason:      the Maker agent misunderstood the team's idempotency pattern.
        Correction:  key the operation on PaymentOperationId, not RequestId.

The team decides whether the correction was specific to that ticket or is reusable knowledge. If it is reusable, it enters the governed path in the Context Governance Standard: a proposed context change, review by the context steward, a test against representative tasks, and only then a versioned, reversible update to the architecture notes, the agent instructions such as AGENTS.md or CLAUDE.md, a skill or a Checker rule.

Conceptual model

Agent learning and context governance

An agent produces an output and a human reviews it. A human correction is classified to determine whether it is a reusable engineering lesson. If it is not reusable, it remains local to the work item. If it is reusable, a context-change proposal records the lesson, affected context and intended validation. A context steward reviews the proposal, including security and architecture invariants. Context tests then evaluate the proposed change. A failed test returns the proposal for revision. A passing change is versioned, reviewed and made reversible before it can affect future runs. Monitoring can create a rollback record that returns the context to an earlier version. Agents do not directly promote their own corrections, edit non-waivable guardrails or bypass the context decision path.

ReviewHuman review identifies whether a correction is local or reusable.
  1. Agent outputImplementation, review result or operational observation from a run.
  2. Human reviewA person inspects the result and identifies a correction or lesson.
  3. Human correctionA specific change to the implementation or reasoning that may contain reusable knowledge.
  4. Reusable lessonA reviewed candidate for a rule, skill, example, test or architecture instruction.
GovernA steward evaluates scope, invariants, tests and ownership.
  1. Context-change proposalNames the change, owner, scope, affected runs, validation plan and rollback path.
  2. Context stewardHuman owner who reviews whether the proposed lesson belongs in durable context.
  3. Invariant reviewChecks that security, release and architectural guardrails remain human controlled.
  4. Context testsEvaluates the proposed context against representative tasks, failure cases and regressions.
  5. RiskRejected context changeA failed evaluation remains inactive and returns with its findings.
Release and learnApproved context becomes available to future runs and can be rolled back.
  1. Versioned contextAn approved, reviewed and reversible release of agent skills, rules or examples.
  2. Future agent runA later run loads the approved context version as part of its execution environment.
  3. Rollback recordA reviewed signal that restores a prior context version when a change causes harm.
Edges
  • Human review leads to Human correctionidentifies correctionFeedback
  • Human correction leads to Reusable lessonclassifies possible lessonFeedback
  • Reusable lesson leads to Context-change proposalopens governed changeGated
  • Context-change proposal leads to Context stewardrequests ownership decisionGated
  • Context steward leads to Invariant reviewchecks protected boundariesGated
  • Invariant review leads to Context testsauthorises evaluationGated
  • Context tests leads to Versioned contextpasses context evaluationGated
  • Context tests leads to Rejected context changerecords failed evaluationFailure
  • Rejected context change leads to Context-change proposalreturns findings for revisionFeedback
  • Future agent run leads to Rollback recordmay reveal regressionFailure
  • Rollback record leads to Versioned contextrestores reviewed versionFeedback
Legend
  • Runtime loopThe high-frequency agent output and human review cycle.
  • Governed context changeA reviewed, tested, versioned and reversible update to future agent context.
  • Human controlPeople decide which lessons become durable knowledge and protect invariants.
  • Rejected context changeA failed evaluation returns to revision and cannot affect future runs.
Human corrections can improve future runs only through a separately reviewed, tested and reversible change path.

Next Monday's planning agents load the new context. The correction was made once, by a person, and the delivery system now knows it. That is what the manifesto means when it says correction should improve the system. The Retrospective Template structures the session; the field that matters most is the one that separates a local fix from a durable lesson.

Exit condition

Every Candidate has been seen by its stakeholder and Human Gate 3 has been decided for it. The sprint is closed. Not-ready items carry their state into Monday's selection. Approved context changes are versioned.

The weekly rhythm

Described publicly, the rhythm is simple:

Table 108
MondayTuesdayWednesdayThursdayFriday
AgentsPlanBuildVerify and remediateFix and re-checkEvidence and summaries
HumansDecide and approveExceptions onlyEscalations onlyValidateRelease and learn
Human loadHighLowLowHighMedium
Human GateHuman Gate 1Execution cutoffHuman Gate 2Human Gate 3

Or even more simply: Monday decides, Tuesday builds, Wednesday proves, Thursday validates, Friday ships and learns.

The other half of the week

The tables above show each person on the days the sprint needs them. On the other days the sprint asks for a bounded amount of preparation, measured in hours, and the rest of the day is that person's normal work.

The preparation is what makes the next iteration better, so it is worth naming:

Table 113
DayLead engineerQA engineerProduct owner
MondayPlan reviewTest strategy review, first regression casesSelection and clarification
TuesdayEscalations only. An hour or two to write and test the context changes approved last FridayRegression cases for this week's items, written from the approved plans before any code existsRefine next week's candidates to Ready
WednesdayEscalations only. Tune Checker rules against this week's failuresFinish the regression additions. Prepare exploratory chartersRefine next week's candidates to Ready
ThursdayEngineering acceptance, most of the dayQA, the afternoonReachable for intent and edge cases
FridayRelease decision and the learning reviewStakeholder session. Learning review. Fold this week's cases into the standing suiteBring the stakeholder. Learning review and a first pass at next week's selection

Three things follow:

  • The learning review has somewhere to land. Friday produces the correction. Tuesday or Wednesday is when the lead engineer writes the skill, architecture note or Checker rule and tests it against representative tasks. Without an hour for that, the learning loop is an intention, and the Repeat Error Rate does not fall.
  • QA moves upstream. Writing regression cases from the approved Build Plans before any code exists tests against intent rather than against the implementation, and it is why Thursday afternoon can be execution rather than design.
  • A quiet Tuesday is quiet for the sprint, not for the person. The capacity the agents free up goes to the next iteration and to everything else the person is responsible for. If Tuesday and Wednesday look idle on the sprint board, that is not an argument for selecting twelve items instead of six on Monday. Thursday's validation capacity has not changed.

Do I need a dedicated team for five days?

No.

You need named people who are accountable for the sprint. They do not need to spend five full working days on it. An engineer allocated to an Agentic Sprint will typically:

  • take part in Monday's planning and approval session;
  • return to their normal engineering work;
  • answer an agent escalation on Tuesday, if one arrives;
  • have little or no involvement on Wednesday;
  • spend meaningful time validating Candidates on Thursday;
  • take part in the Friday release decision;

while continuing other work throughout the week. This is intentional. The system is designed around asynchronous human intervention rather than continuous human supervision, which is what makes it workable for an organisation running Agentic Sprints alongside normal business-as-usual.

One distinction matters. "Not dedicated full-time" does not mean "nobody owns it". Every sprint still has clearly named humans responsible for:

  • scope and intent: the product owner;
  • technical approval: the lead engineer, at Human Gate 1 and Human Gate 2;
  • QA and validation: the QA engineer;
  • release authority: the person who decides at Human Gate 3.

Accountability is dedicated. Human utilisation is not.

Board states and capacity

Board states

The board has five states a work item passes through, plus two exits. Each state is entered by a human decision or an agent verification, never by an agent's own status report:

Conceptual model

Board states of a 5-Day Agentic Sprint

The board states form a funnel. Selected items are locked on Monday morning by human sprint selection. Authorised items have a Build Plan approved at Human Gate 1 on Monday afternoon. Candidate items have passed the independent checker with complete evidence before the Wednesday execution cutoff. Accepted items have passed Human Gate 2, Engineering Implementation Acceptance, and independent QA on Thursday. Released items have been validated by the most impacted business stakeholder and passed Human Gate 3, Release Authority, on Friday. An item can leave the funnel at any state as deferred, carrying its recorded state into next week, or rejected, returning to discovery. No state is entered by an agent's own status report. Agentic Sprint Capacity is the number of items the team can responsibly carry from selected to accepted in one week.

  1. SelectedMonday morning. Locked by human selection to fit review capacity.
  2. AuthorisedMonday afternoon. Build Plan approved at Human Gate 1.
  3. CandidateBy the Wednesday cutoff. Checker-approved with complete evidence.
  4. AcceptedThursday. Human validation: Human Gate 2 and independent QA passed.
  5. ReleasedFriday. Stakeholder validation, then Human Gate 3: ship, hold or reject.

Each state is a count of work items that passed a decision, not a count of agent output. Report the counts and where the rest stopped.

Legend
  • Board stateA count of work items that passed a decision, never a count of agent activity.
  • ExitDeferred carries state to next week. Rejected returns to discovery. Both record why.
Five states, each entered by a decision. The sprint is measured by how many selected items became released.
Table 128
StateEntered byMeaning
SelectedMonday selectionIn this week's Agentic Sprint Backlog with a named owner
AuthorisedHuman Gate 1Build Plan approved, and a Maker agent may begin
CandidateChecker agent verificationImplementation verified and evidence complete before the Wednesday cutoff
AcceptedHuman Gate 2 and QAHuman validation passed: engineering accepted and behaviour validated
ReleasedHuman Gate 3Shipped, or held with a recorded reason
Not readyWednesday cutoffDid not reach Candidate. Carried to next week with recorded state and evidence
RejectedAny gateReturned to discovery with the reason recorded

An orchestrator can show finer states underneath, such as which goal a Maker agent is on. The five human states are the ones the team reports.

Sprint capacity

Do not ask how much the agents can build this week. Ask how much agent-produced work the humans can deeply validate this week.

Sprint capacity is human validation capacity, not agent generation capacity. Agentic Sprint Capacity is the number of independently verifiable outcomes the team can responsibly validate, accept and release within the week.

If the agent fleet could generate thirty implementations but engineering and QA can properly validate six, sprint capacity is about six. Not tokens. Not agent-hours. Not story points. Not generated pull requests. Validated, accepted outcomes. Agent compute is elastic. Human judgement is scarce. Do not flood the human gate.

A useful first estimate is the number of Candidates the lead engineer can validate on Thursday at 45 to 60 minutes each, checked against the number the QA engineer can validate the same afternoon, and reduced for any R2 or R3 item that needs a security review. Most teams find the answer is a single digit. That is not a limitation of the agents. It is an honest measure of the team, and it is Principle 10 of the Manifesto, measure accepted outcomes rather than raw activity, applied to planning.

Work-in-progress limits

The operating model recommends limiting the number of changes awaiting human decisions. In this cadence the natural limits are:

  • plans awaiting approval on Monday afternoon: the sprint backlog and no more;
  • items in execution at once: the sprint backlog, or fewer for R2 and R3 work;
  • Candidates awaiting engineering review on Thursday: what one day of validation can absorb;
  • items awaiting QA: what the QA engineer can validate on Thursday afternoon;
  • context changes awaiting the steward: cleared each Friday.

If any queue grows past its limit, the answer is to select less on the following Monday, not to add Maker agents.

Metrics for the week

The Metrics Framework defines the measures. The cadence needs only a handful, reported as a funnel every Friday:

Table 141
MeasureDefinitionWhat it tells the team
SelectedItems locked on Monday morningThe week's commitment
AuthorisedPlans approved at Human Gate 1Requirement and plan quality
CandidateItems verified by the Checker agent by the Wednesday cutoffExecution and context quality
AcceptedItems through Human Gate 2 and QAImplementation fit
ReleasedItems through Human Gate 3Delivered outcomes, the throughput measure
First-Pass Plan AcceptancePlans approved on Monday without revision, divided by plans reviewedWhether the versioned context and the readiness check are working
Review LoadHuman validation minutes per accepted itemWhether Thursday is sustainable
Repeat Error RateCorrections that recur from a previous weekWhether Friday's learning review is working

Report the counts, not a ratio alone, and report which items stopped at which state and why. A week with six selected, six authorised, five Candidates, four accepted and four released is a good week. A week with fifteen selected and four released is a planning failure, however busy the agents were.

Adapting the cadence

The five-day shape is a default, not a rule. Some adaptations keep the structure:

  • Two-week cadence. Monday selection and Friday release stay where they are and the middle stretches. Keep the execution cutoff at least two working days before release, so that validation is never compressed into the last afternoon.
  • Rolling release. A team with continuous deployment can hold Human Gate 3, and the stakeholder validation before it, on any day. Keep the Friday learning review, because that is the ceremony that compounds.
  • Higher-risk domains. R2 and R3 items may need Monday's plan review to include the security owner and Thursday's validation to include an architecture reviewer. Reduce the selected count to keep Thursday possible.
  • Very small teams. One engineer can hold the lead engineer, QA and release roles for low-risk work, but the decisions are still recorded separately and the Checker is still a separate agent.
  • Mixed human and agent work. Items that are not ready for agentic execution can run beside the agentic backlog in the conventional way. Keep them off the agentic board so that the funnel stays honest.

What this document does not claim

This cadence does not claim that a team will ship six items a week, that Claude Code or Codex will produce Candidates without correction, or that a one-week cycle suits every product. It does not claim that Thursday's validation can be shortened because the Checker agent passed. It describes one way to arrange the week so that the methodology's gates fall on days when the right people are in the room, so that people are allocated to the sprint without being consumed by it, and so that generated work never outruns the people accountable for validating it.

Document status and limitations

This is version 0.1 of a proposed operating cadence in the Agentic Sprint Methodology series. It is informative: it adds no requirements to D1 and can be adopted, adapted or ignored without affecting conformance. The day-by-day timings and engagement levels are a planning default, not a measured result. Claude Code and Codex are named as examples of tools that can hold the planning, Maker and Checker roles. The cadence does not depend on either, and no claim is made about their performance. No observed throughput, quality or cost result is implied.

Sources

  1. code.claude.com/docs/en/overviewcode.claude.com
  2. developers.openai.com/codexdevelopers.openai.com
  3. git-scm.com/docs/git-worktree.htmlgit-scm.com

Corrections and material updates are dated on this page and recorded in the document status.