SDLC blueprint

Forge — roles (prescriptive)

Forge keeps standard role names and uses the blueprint's **Owner / Implementer** model. It adds a **hat-switching protocol** for teams where one person holds multiple perspectives and **Versonas** as

Forge — roles (prescriptive)

Forge keeps standard role names and uses the blueprint's Owner / Implementer model. It adds a hat-switching protocol for teams where one person holds multiple perspectives and Versonas as challenge functions (not roles).

0. Forge team vs sub-accountabilities

A Forge team consists of people who own, implement, challenge, and govern work. In small teams these accountabilities overlap. Forge makes the overlap explicit through hats rather than pretending one person is not making trade-offs between perspectives.

0.1 Map to this blueprint (foundation)

Foundation element Product hat Engineering hat Challenge hat Governance hat
Phases A–F Strong in A–B (discover/specify) Strong in C–E (design/build/verify) Cross-cutting (all phases) Strong in E–F (verify/release)
Tracking spine Owns value ordering and acceptance Links work to Ingots/Sparks; meets DoD Reviews for blind spots Ensures traceability and evidence
Ceremony intents C1–C6 C1/C2 in refinement & planning C2–C4 execution and review C1/C4 challenge at decision points C6 Assay Gate steward
Archetypes Sponsor proxy, Orchestrator Implementer, Quality advocate Quality advocate (cross-cutting) Orchestrator, Quality advocate

0.2 Versonas: challenge functions, not roles

Versonas are not team roles. They are discipline-specific challenge agents (AI or human) that pressure-test work from particular perspectives. A Versona challenge does not own delivery; it strengthens thinking.

Versona family Disciplines covered When most active
Engineering SE, Architecture, DevOps, Testing, Frontend, Mobile, Embedded/IoT Design, build, verify
Data Big Data, Data Science Specify, design, verify
Product BA, UX, Marketing, Customer Success Discover, specify, review
Governance PM Planning, review, release
Cross-cutting Security, Compliance All phases (shift-left emphasis)

Each Versona references its discipline's knowledge base and bridge document in ../../disciplines/.

1. The four hats

Product hat

Aspect Prescriptive guidance
Accountable for Maximizing value from the team's work; Ore intake quality; Ingot acceptance
Key question Is this the right thing to build?
Typical archetype Sponsor proxy, Orchestrator
Outputs Ore items with context, ordered Ingots, acceptance decisions, Product Spark definitions

Engineering hat

Aspect Prescriptive guidance
Accountable for Technical quality, implementation correctness, sustainable architecture
Key question Is this built well?
Typical archetype Implementer, Quality advocate
Outputs Completed Sparks meeting DoD, PRs, technical ADRs, test coverage

Challenge hat

Aspect Prescriptive guidance
Accountable for Surfacing blind spots, risks, and alternatives before costly commitments
Key question What are we missing?
Typical archetype Quality advocate (cross-cutting)
Outputs Versona challenge results, risk flags, alternative proposals, evidence requests

Governance hat

Aspect Prescriptive guidance
Accountable for Release evidence, process discipline, decision traceability
Key question Can we prove this is ready?
Typical archetype Orchestrator, Quality advocate
Outputs Assay Gate checklists, Ember Log stewardship, release decisions, compliance evidence

2. Hat-switching protocol

In smaller teams (especially solo practitioners), one person holds multiple hats. Forge makes this explicit:

  1. Declare the hat. Before a decision, state which perspective you are prioritizing. In conversation or commits, prefix with the hat: [Product], [Engineering], [Challenge], [Governance].
  2. Separate the concerns. Do not mix "should we build this?" (Product) with "how should we build this?" (Engineering) without marking the transition.
  3. Use Versonas for hats you are not wearing. When deep in Engineering, invoke a Versona agent for the Product or Challenge perspective you might neglect.
  4. Log hat-switching decisions. When a hat-switch changes direction (e.g., Engineering hat overrules Product hat on feasibility), capture the trade-off in the Ember Log.

3. Scaling model

Scale Team size Hat distribution Versona configuration
Solo 1 One person, all hats, explicit switching AI Versonas for all uncovered perspectives
Small team 2–5 Hats shared; designate primary per person AI Versonas + peer challenge
Team 5–12 Dedicated hat holders possible; Versona disciplines assigned Mix of AI and specialist human Versonas
Multi-team 12+ Full role separation; cross-team Versonas Dedicated challenge roles + AI augmentation

Scaling rule: add formality only when the cost of a mistake exceeds the cost of the ceremony.

4. Event participation matrix

Legend: R = required · O = optional · = not expected

Ceremony Product hat Engineering hat Challenge hat Governance hat
Refinement (Ore → Ingot) R R O (Versonas) O
Planning (Ingot → Sparks) R R O (Versonas) O
Daily sync (Charge) O R
Review (evidence) R R R (Versonas) R
Assay Gate (release) R R O R
Retro (learning) R R O R

5. Interface with standard roles

Standard role Forge hat mapping
Product Owner / PM Product hat (primary); Governance hat (release acceptance)
Engineering Lead / Architect Engineering hat (primary); Challenge hat (technical review)
Scrum Master / Delivery Lead Governance hat (primary); facilitates ceremonies
Developers / Engineers Engineering hat (primary)
QA Challenge hat (testing Versona); Governance hat (Assay Gate evidence)
BA / Analyst Product hat (Ore intake, Ingot refinement); Challenge hat (BA Versona)

6. Anti-patterns (by hat)

Anti-pattern Why it hurts Fix
Never declaring hat switches Conflated decisions; no audit trail Explicit [Hat] prefix; Ember Log
Challenge hat dominates every decision Analysis paralysis; Versonas as bureaucracy Time-box Versona; use at decision points only
Governance hat skipped for speed Undocumented decisions; release surprises Assay Gate is non-negotiable; adjust scope instead
Solo developer ignores all but Engineering Product/challenge blind spots compound Schedule Versona invocations; daily hat rotation

7. Agentic SDLC (same accountabilities)

AI agents (including Versonas) do not replace human accountability for Ore acceptance, Spark quality, release decisions, or Ember Log integrity. Agent output is treated like any other contribution: traceable, reviewed, covered by DoD. See agentic-sdlc.md.

Canonical source

Edit https://github.com/autowww/blueprints/blob/main/sdlc/methodologies/forge/roles.md first; regenerate with docs/build-handbook.py.