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Architecture Intent

Strategic Vision

  • What we are building: A composable, lightweight operating architecture for Enterprise Architecture automation that transforms proven EA operating model elements into reusable, governed workflows.
  • Why it exists: To make EA practice more evidence-led, repeatable, and scalable while preserving human judgment, accountability, and stakeholder engagement for consequential decisions.

Project Context

primary_context: ea4all-automation aw_count: 6 alignment_findings: 3

Current State Alignment

The system is partially aligned with strategic intent. Core empirical evidence coverage is high, but governance maturity is nascent. The architecture currently exhibits a structural monolith in the core workflow and relies entirely on inherited global CAS ADRs, creating a gap between accepted strategic decisions and operational reality.

Focus Areas / Roadmap

  • Initialize Project Runtime Capability Inventory to establish deterministic baseline and track implementation deltas.
  • Establish Project-Specific Architecture Decision Record Framework to capture local workflow orchestration and evidence intake decisions.
  • Refine Component Boundary Decomposition for Core Workflow by splitting the monolithic component into discrete intake, classification, and structured synthesis modules.
  • Operationalize Evidence Isolation and Forensic Diagnostic gates to align CI-level validation with accepted ADRs.
  • Validate conceptual architecture blueprint through real delivery, iterating on methods and generalizing successful automation candidates into reusable SOPs and Skills.

Gaps & Tensions (Risks)

  • Absence of project-scoped ADRs obscures local trade-offs and violates single-source-of-truth standards.
  • Lack of authoritative runtime capability inventory prevents automated delta verification and deterministic extraction.
  • Structural monolith in the core workflow contradicts the composable pipeline vision and separation of concerns.
  • Governance framework adoption (ADR-067, ADR-075) is accepted but operationally pending, creating a temporary enforcement gap.

Principles

  • Start with a real delivery problem, not a technology opportunity.
  • Formulate a clear hypothesis before automating.
  • Build the smallest useful solution and validate it in real delivery.
  • Evidence before narrative; make uncertainty explicit.
  • Use AI as augmentation, not as unaccountable authority.
  • Keep human review mandatory for consequential judgement.
  • Prefer guardrails and actionable feedback over gatekeeping.
  • Learn, iterate, generalise, replace, or stop each automation based on demonstrated value.

Emergent Heuristics

decision_tracing: Material decisions must be traceable from context and evidence to consequences. automation_threshold: A repeatable delivery problem must show sufficient frequency, effort, risk, or value before it is generalised into a platform capability. governance_evolution: Shift from global CAS dependency to project-specific governance as local evidence and decisions accumulate. workflow_adaptation: Workflow depth and automation scope must be tailored to decision risk, complexity, and available evidence, not forced into rigid templates.