Roadmap

Purpose

This roadmap describes the planned evolution of the Engineering Knowledge Framework.

Unlike the Strategy, which defines the long-term vision, this document captures the major initiatives required to realize that vision.

The roadmap should evolve continuously as the project matures.

Guiding Principles

Roadmap priorities should always support the framework's mission:

Priority should always be given to work that strengthens the framework itself before expanding its content.

Phase Progression

Progress through phases is not strictly sequential. Work may overlap between phases, especially for cross-cutting initiatives like AI integration and tooling.

Transition Criteria

A phase is considered complete when:

Current Phase

The project has completed Phase 1 (Foundation), Phase 2 (Foundation Capabilities), Phase 3 (Engineering Capabilities), and Phase 4 (AI Engineering Framework). All five Phase 3 capabilities — Rails Engineering, Security Engineering, AI Engineering, Engineering Quality and Accessibility Engineering — are now built out with all nine document types covered.

The Learning Framework (Phase 5) has learning paths for all seven capabilities and needs practical exercises and assessments.

Framework Tooling (Phase 6) and the Static Site (Phase 7a) are substantially built out — validation tools, knowledge graph generation, Zola-based site with full-text search, and GitHub Pages deployment are all operational. Remaining tooling work focuses on generator tooling and deeper AI integration.

See the CHANGELOG for the detailed deliverable list.

Phase 1 — Foundation

Establish the governance and architectural foundations of the framework.

Status

[x] Complete — All governance documents, repository structure, document taxonomy, and capability stubs are in place. Phase 1 transition criteria are satisfied.

Objectives

Deliverables

Success Criteria

Lessons Learned

Phase 2 — Foundation Capabilities

Develop foundational engineering capabilities that establish patterns for all future capabilities.

Engineering Fundamentals and Software Architecture are the initial foundation capabilities. They are chosen because engineering principles apply across all capabilities and architecture concepts are referenced by every engineering discipline.

Status

[x] Complete — All deliverables published, validated, and reviewed. Phase 2 transition criteria are satisfied. These capabilities now serve as reusable templates for Phase 3.

Objectives

Build comprehensive documentation for Engineering Fundamentals and Software Architecture that:

Deliverables

2a — Engineering Fundamentals

2b — Software Architecture

Practices

Workflows

AI Workflows

Success Criteria

Lessons Learned

Phase 3 — Engineering Capabilities

Expand the framework to additional engineering disciplines using the Engineering Fundamentals and Architecture capabilities as templates.

Status

[x] Complete — All five engineering capabilities are built out across all nine document types (handbook, glossary, guides, playbooks, checklists, templates, learning paths, references, AI workflows). Phase 3 transition criteria are satisfied. These capabilities now form the reusable knowledge foundation for future engineering disciplines.

Objectives

Capability Breakdown

[x] Rails Engineering — Complete (all 9 document types)

[x] Rails Engineering — Complete (all 9 document types)

[x] Security Engineering — Complete (all 9 document types)

[x] AI Engineering — Complete (all 9 document types)

[x] Engineering Quality — Complete (all 9 document types)

[x] Accessibility Engineering — Complete (all 9 document types)

Future Capabilities

Additional capabilities to be developed as the community grows:

Development Approach

Each capability should:

  1. Define its scope and boundaries.
  2. Identify related capabilities and cross-references.
  3. Build incrementally: handbook → glossary → guides → playbooks → checklists → templates → learning paths → references → AI workflows.
  4. Include AI workflows alongside traditional documentation.
  5. Pass style and link validation before release.

Priority should follow organizational need rather than attempting to build all capabilities simultaneously.

Lessons Learned

Phase 4 — AI Engineering Framework

Develop AI-native engineering practices that integrate AI throughout the engineering lifecycle.

Status

[x] Complete — The AI Engineering capability is fully built out across all nine document types. All six cross-cutting AI topics (agentic workflows, collaboration patterns, human review strategies, evaluation frameworks, verification workflows, knowledge extraction) have been developed as guides and playbooks. Domain-specific AI workflows exist for Engineering, Architecture, Rails, Security, Quality and Accessibility. All Phase 4 deliverables are published.

Objectives

Completed

Completed (added)

Long-term Goal

Provide practical guidance for integrating AI throughout the engineering lifecycle, establishing AI-assisted engineering as a core organizational capability.

Phase 5 — Learning Framework

Build structured learning experiences for every capability.

Status

[x] In Progress — Beginner, intermediate and advanced learning paths exist for Engineering, Architecture, Rails, Security, AI Engineering, Engineering Quality, and Accessibility Engineering. All seven capabilities have structured learning paths with topics, suggested projects and assessment criteria. The next step is to add practical exercises aligned with each level, formal competency assessments, and cross-capability learning sequences.

Objectives

Develop standardized learning paths that support progressive skill development.

Completed

Next Steps

Each capability should eventually include:

The framework should support both self-paced learning and organizational training.

Lessons Learned

Phase 6 — Framework Tooling

Develop tooling that improves authoring, validation and discovery.

Status

[x] Substantially Complete — Validation tooling (6a) and knowledge graph tooling (6c) are fully implemented. Generator tooling (6b) remains to be built. All tooling runs locally via make commands and in CI via GitHub Actions.

Objectives

Deliverables

Phase 6a — Validation Tooling (Complete)

Initial tooling focused on correctness and consistency:

Phase 6b — Generator Tooling (Not Started)

Tooling for scaffolding new capabilities and documents:

Phase 6c — Knowledge Graph Tooling (Partial)

Tooling for discovery and navigation:

Development Approach

  1. Build validation tooling first — it establishes quality gates that all other work must pass.
  2. Use existing documents as test fixtures for validation rules.
  3. Generators should produce templates that pass validation by default.
  4. Knowledge graph tooling depends on consistent metadata and cross-references, so it comes after validation.
  5. All tooling should run locally (CLI) and in CI.
  6. Prefer standalone scripts over framework dependencies.

Tooling should automate repetitive work while preserving editorial quality.

Lessons Learned

Phase 7 — Platform

Expand the framework into a complete engineering knowledge platform.

Status

[x] In Progress — A Zola-based static site (Phase 7a) is fully operational with custom theme, full-text search, and GitHub Pages deployment via GitHub Actions. The static site serves all framework content with navigation by capability and document type. Remaining platform work focuses on the knowledge graph UI, AI integration, and advanced platform features.

Objectives

Deliverables

Phase 7a — Static Site (Complete)

Phase 7b — Knowledge Graph UI

Phase 7c — AI Integration

Phase 7d — Platform Features

Development Approach

  1. Static site was built alongside Phase 6 tooling — validation and site generation share the same CI pipeline.
  2. Knowledge graph UI depends on Phase 6c (graph generation — already built).
  3. AI integration should use the knowledge graph as its data source.
  4. Community portal is last — it requires critical mass of users.

Lessons Learned

Phase 8 — Community

Grow an active open-source community around the framework.

Status

[ ] Not Started — Community growth depends on having stable documentation and tooling that others can build on. The CONTRIBUTING.md document establishes initial contribution guidelines. GitHub Issues and Discussions are enabled.

Objectives

Deliverables

Onboarding

Review and Quality

Engagement

Development Approach

  1. Start with clear contribution guidelines and low-friction PR processes.
  2. Use good-first-issue labels to lower the barrier to entry.
  3. Build maintainer capacity before scaling contributor base.
  4. Community contributions should follow the same quality standards as core contributions.
  5. Recognise and celebrate community contributions.

The framework should evolve through practical engineering experience rather than theoretical completeness.

Phase 9 — Open Standard

Establish the Engineering Knowledge Framework as an open standard for engineering knowledge management.

Status

[ ] Future — This phase depends on community adoption and real-world validation. The framework must prove itself across multiple organizations before standardisation is meaningful.

Objectives

Deliverables

Specification

Ecosystem

Governance

Development Approach

  1. Standardisation should follow adoption, not precede it.
  2. Extract the specification from the reference implementation.
  3. Keep the specification minimal — define only what must be interoperable.
  4. Version the specification independently of the framework content.
  5. Provide migration guides for breaking changes.

Cross-cutting Initiatives

The following initiatives span multiple roadmap phases.

AI Integration

Every capability should eventually include:

Continuous Improvement

Regularly refine:

Lessons learned from adopting the framework should feed back into the framework itself.

Knowledge Preservation

Prioritize capturing:

Institutional knowledge should become reusable organizational assets.

Measuring Progress

Progress should be measured by:

Document count is not a success metric.

Near-term Priorities

Current priorities are:

  1. Build Engineering Fundamentals capability (Phase 2a).
  2. Build Software Architecture capability (Phase 2b).
  3. Create reusable document templates for all document types.
  4. Develop Rails Engineering capability (Phase 3).
  5. Develop Security Engineering capability (Phase 3).
  6. Develop AI Engineering capability (Phase 3/4).
  7. Develop Engineering Quality capability (Phase 3).
  8. Develop Accessibility Engineering capability (Phase 3).
  9. Establish RFC and ADR processes for framework evolution (see adr/ directory and ADR template).
  10. Add practical exercises, formal assessments and cross-capability learning sequences to learning paths (Phase 5).
  11. Develop AI agentic workflows and evaluation frameworks (Phase 4).
  12. Build validation tooling — link validation, style validation, glossary validation, TOC validation (Phase 6a).
  13. Build knowledge graph generation tooling (Phase 6c).
  14. Build generator tooling — capability scaffolding, document type generators (Phase 6b).
  15. Build knowledge graph UI visualization (Phase 7b).
  16. Build AI context packaging for AI assistants — CLI tool + MCP server (Phase 6c/7c).
  17. Explore community engagement and contribution workflows (Phase 8).

These initiatives build on the completed foundation. See CHANGELOG for completed milestones.

Future Evolution

This roadmap intentionally focuses on enduring direction rather than specific release schedules.

As the framework matures, priorities will evolve based on:

The roadmap should remain flexible while preserving the framework's core mission.

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