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ForgeSDLC Adoption Playbook

A step-by-step guide for teams adopting ForgeSDLC. Whether you're starting a greenfield project or migrating from an existing methodology, this playbook walks you through the process.

Sponsors and change leads: Adoption for sponsors (hub) · Executive overview · Enterprise change management. Framework migration: Scrum · Kanban · Waterfall · SAFe-adjacent.

Phase 1: Foundation (Week 1)

Set up the blueprint submodule

git submodule add https://github.com/autowww/blueprints.git blueprints

This gives your project access to the full body of knowledge: SDLC phases, PDLC lifecycle, discipline-specific templates, and ceremony definitions.

Establish your WBS hierarchy

ForgeSDLC uses a clean hierarchy: Milestone → Epic → Story → Task (Spark). Each level has a clear purpose:

Level Purpose Cadence
Milestone Business outcome Quarterly
Epic Capability delivery Monthly
Story User-visible increment Weekly
Spark (Task) Atomic work unit Daily

Choose your initial disciplines

Don't adopt all blueprints at once. Start with the 2-3 most relevant to your project:

  • Every project: SDLC blueprint (phases, ceremonies, tracking)
  • Web/mobile apps: Frontend, Testing, DevOps
  • Data products: Data Science, BigData, Testing
  • Platform teams: Software Architecture, DevOps, Security

Phase 2: First Sprint (Weeks 2-3)

Run your first Versona session

Pick a real decision your team needs to make (architecture choice, deployment strategy, testing approach). Run a Versona session (invocation with that virtual persona):

  1. Identify the discipline — which body of knowledge applies?
  2. Pull the blueprint — read the relevant section of the discipline blueprint
  3. Apply the knowledge — use the templates, checklists, and quality gates
  4. Log the decision — record the outcome in your forge journal
  5. Continue work — the session is done; return to Spark execution

Establish the Charge view

Set up a simple Charge view showing: - Active Sparks (who/what is working on them) - Blocked Sparks (what decision is needed) - Pending Versona sessions (what is queued)

Charge is the daily selected set of Sparks—a decision-oriented view on work, not a parallel artifact taxonomy. Many teams still use a board or tracker for visibility; Forge does not require replacing that tooling. The daily sync confirms Charge and surfaces blockers—see the Forge meetings model for the full meeting set and ownership rules, and Forge ceremonies (prescriptive) for inputs and outputs per meeting.

Phase 3: Calibration (Weeks 4-6)

Apply the full principles set

Review your process against the execution principles and lean tenets on Forge principles (full list) (and keep the four core trade-off rules—shape, flow, AI-first human-gated, compounding—in mind; see the blog). Remove anything that adds weight without preventing waste. Common removals:

  • Status-only standups (replace with a lean daily sync focused on Charge, blockers, and decisions)
  • Long generic retros (replace with brief regular retros plus milestone reviews when warranted)
  • Velocity theater (replace with flow, decision quality, and release-evidence metrics)

Rule: Keep the daily sync. Use Versona sessions and small decision huddles as follow-ups when a real discipline decision is needed.

Expand discipline coverage

Add 1-2 more discipline blueprints based on where your team needs the most knowledge support.

Phase 4: Steady State (Week 7+)

At this point ForgeSDLC should feel natural. Core ceremony intents (alignment, commitment, sync, inspection, improvement, assurance) stay on a lean default cadence—including a short daily sync and a regular retro—while discipline Versonas and decision huddles spin up when a real decision needs them. Blueprints provide the knowledge backbone. Sparks flow through to completion without unnecessary checkpoints.

Ongoing health checks

  • Principle compliance: Monthly review of the full principles list (Forge principles)
  • Blueprint freshness: Are the discipline blueprints still relevant?
  • Ceremony ROI: Is every Versona session producing a decision?
  • AI integration: Are AI agents effectively executing Sparks?