AI-Powered Stack — Sprint zero for XM Cloud with agentic AI

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Sitecore teams are smaller than the project scopes they carry. I stopped fighting that reality and built an AI-powered Sprint-Zero playbook where agents act like a rotating bench of business analysts, architects, estimators, and project managers. The approach below is the one I run in 2025 for every XM Cloud + Next.js + Experience Edge engagement, from pre-sales discovery to the moment we hand off signed scope, diagrams, epics, and stories to delivery.

At a high level my Sprint Zero pipeline looks like this:

Client brief

Ground truth workspace

NotebookLM + Azure OpenAI On Your Data

Recon & component matrix

Playwright + agents

Information architecture & epics

Claude Code + ChatGPT Pro

Backlog & estimates

ALM tools + agents

Environments & serialization

XM Cloud docs

Pricing & scope

Estimator agent

Sprint Zero handoff

Capturing the brief and building a ground truth workspace

Goal: load every scrap of context into a private knowledge base before the first planning session.

  1. Drop the request for proposal (RFP), stakeholder notes, past audits, and customer relationship management (CRM) transcripts into NotebookLM so I can query them conversationally without leaking data off-domain. The “Audio Overview” feature is perfect for executive summaries and feeds the same notebook I share with our internal project manager.
  2. Mirror that corpus inside Azure OpenAI On Your Data, backed by Azure AI Search with role-based access control and private networking. That keeps later prompts grounded, enforces citations, and respects the guardrails Microsoft documents in the Use your data guidance.
  3. Stand up a simple taxonomy spreadsheet (“journeys, personas, key performance indicators”) that both NotebookLM and the On Your Data index can reference. This becomes the anchor for every question our agents answer later in the sprint.

Artifacts produced: curated NotebookLM notebook, Azure OpenAI On Your Data index definition, taxonomy spreadsheet.

Letting agents recon the current estate

Goal: know the site better than the client by Day 2.

  1. Run a Playwright crawler (wrapped in a Model Context Protocol endpoint) against the legacy site to capture URLs, headings, ARIA roles, and component usage. This JSON powers our AI-generated component matrix.
  2. Hand the crawl + sitemap to a “Research Analyst” prompt in Claude Code. It classifies pages, flags outdated patterns, and suggests content gaps to confirm with stakeholders.
  3. In parallel, capture the digital marketing stack (Customer Data Platform, personalization rules, existing Sitecore content) so agents can reason about downstream integrations.

Artifacts produced: annotated sitemap, component frequency report, competitor snapshot.

Converting reconnaissance into requirements and information architecture

Goal: go from messy crawl data to a structured backlog skeleton.

  1. Feed the Playwright JSON into ChatGPT Pro’s Codex mode with a prompt that emits components/<Name>.md files (props, datasource, analytics, accessibility) aligned with the official Components in XM Cloud terminology.
  2. Ask Claude Code’s “Business Analyst” persona to draft epics and high-level acceptance criteria per persona/journey, citing the NotebookLM sources used.
  3. Use Gemini 1.5 Flash for quick IA diagram drafts; I export them as SVG and drop them into the briefing deck.

Artifacts produced: component matrix repo, information architecture diagrams, epic list with acceptance criteria.

Spinning requirements into a backlog and delivery model

Goal: land on a delivery model we can price with confidence.

  1. Transform the epics into user stories inside Jira or Azure DevOps via their REST APIs. My “Backlog Clerk” agent maps each story to a swim lane (composable build, Content Hub enablement, search) so reporting stays clean.
  2. Apply sizing heuristics (S/M/L) that relate to our standard throughput. The agent proposes the first pass; I adjust anything that crosses compliance or integration boundaries.
  3. Queue up dependencies in a shared RAID log so presales and delivery see the same risk posture.

Artifacts produced: Jira or Azure DevOps backlog with linked epics, estimation worksheet, RAID log.

Defining the technical runway: environments, architecture, and keys

Goal: remove infrastructure ambiguity before we price.

  1. Map out the Experience Edge plan using Sitecore’s best-practice split between Delivery and Preview endpoints as described in the Experience Edge best practices. AI agents keep a table of where each key lives (local dev, XM Cloud preview, production) and how it will rotate.
  2. Document the Next.js App Router + Content SDK pattern we’ll use for React Server Components, referencing Sitecore’s Next.js App Router Content SDK guide so everyone sees how route handlers fetch data server-side.
  3. Capture non-negotiable constraints: CDN, identity provider, Content Hub integrations, Search JS SDK usage.

Artifacts produced: architecture diagram, environment/key matrix, technical assumptions doc.

Locking down serialization and governance boundaries

Goal: avoid surprises once we start pulling content.

  1. Draft the Sitecore Content Serialization (SCS) module structure with an “Architect” agent referencing the SCS structural overview. We define which items belong in each module, default allowedPushOperations, and when Deletes are even allowed.
  2. Hand the module map to a “DevOps” prompt that writes CLI scripts for ser pull, ser diff, and ser push with the correct safeguards from the Sitecore CLI serialization command.
  3. Capture approval workflows (who signs off on schema updates, who rotates keys) so presales does not promise what governance will block.

Artifacts produced: SCS module diagrams, CLI runbooks, governance RACI.

Pricing and packaging the work

Goal: turn the research into a bid clients trust.

  1. Ask Claude Code’s “Estimator” prompt to combine story sizes, environment work, and risk buffers into a forecast. I keep the human override at 30% because AI still misses organizational drag.
  2. Build the exec deck with NotebookLM’s summaries, IA diagrams, and the architecture board. The deck is agent-drafted but human-edited for tone.
  3. Generate a scope appendix listing every artifact we will hand over in Sprint Zero (component repo, backlog export, RAID log, governance doc). This is the sheet that gets attached to the Statement of Work.

Artifacts produced: estimate model, executive presentation, scope appendix.

Handing off and starting Sprint Zero day one

By the time the client signs, I already have:

Sprint Zero kicks off by validating assumptions with stakeholders, replaying Crawl → IA → Backlog in front of them, and demonstrating the Next.js proof of concept hitting Experience Edge Preview to prove the pipelines. Because the agents drafted everything with citations, we can show how each recommendation ties back to Sitecore’s own guidance on Experience Edge usage, Next.js App Router integrations, and SCS boundaries.

The net effect: presales stays lean, delivery inherits clean documentation, and AI handles the repetitive churn while humans focus on negotiation, architecture judgment, and relationship building.

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