03 · Delivery

Requirements-to-Release Delivery

Traceable delivery connecting requirements, acceptance criteria, test evidence, dependencies and release decisions.

Proposed extension stack Azure DevOpsSharePoint Power AutomateEnterprise LLM

Case scope: The enterprise process, systems and control foundation reflects hands-on delivery patterns. AI and agent components shown below are proposed design extensions.

Linked requirementsEvidence tracking Dependency checksRelease approval

At a glance

Business problemRequirements, evidence and sign-offs are spread across Azure DevOps, documents and stakeholder conversations, making release risk hard to see early

My contribution Requirements lifecycle · acceptance criteria · UAT evidence · release readiness

Current environmentAzure DevOps · SharePoint · UAT evidence · release sign-offs

Business valueRequirements, evidence, dependencies and approvals stay linked through release.

Proposed AI extension · AI / Agent / Human

Finds gaps across requirements, acceptance criteria, dependencies and evidence before they become release issues.

Agent

Keeps readiness checks, blockers and outstanding actions moving across the release workflow.

Human

Product, QA and business stakeholders retain release approval. Overrides and reasons are captured.

Current workflow

01Discovery

02User stories

03Acceptance criteria

04Build support

05UAT

06Release

What I owned

Requirements lifecycleBacklog and story definition Acceptance criteriaDependency tracking UAT planningEvidence requirements Sign-off traceabilityRelease readiness AI use-case definitionAI output requirements

Proposed AI-enabled architecture

Azure DevOps + SharePoint→ Power Automate→ Readiness rules→ Enterprise LLM→ Readiness queue→ Owner action→ Human release approval→ Decision logged

Technical design

MVP scenarios

Scenario 1Complete story. Required fields, acceptance criteria, UAT evidence and sign-off are all present.Ready

Scenario 2Missing mandatory evidence. Required UAT evidence or approval is absent. Deterministic rule flags the gap.Fail

Scenario 3Ambiguous requirement. Acceptance criteria is unclear or untestable. AI explains the gap and routes it for clarification.Review

Scenario 4Downstream impact. AI identifies a likely dependency or component affected by the change and surfaces the risk to the owner.Review

Scenario 5Delayed dependency. A story marked complete depends on another story now delayed. The workflow flags inherited release risk and routes it to the owner.Blocked

Controls and governance

Mandatory readiness rules remain deterministic.

AI does not mark a story release-ready.

Azure DevOps story IDs must be linked consistently to SharePoint evidence and sign-offs.

Missing evidence triggers a review request, not inference.

Human approval remains mandatory for all release decisions.

Overrides and reasons are logged for audit and traceability.

Implementation note

Azure OpenAI is the proposed LLM for identifying ambiguity and risk in requirements and readiness evidence.