The Book · Chapter 18

Executable Architecture in the Field

Executable architecture must survive contact with enterprises that already possess EA platforms, delivery pipelines, software catalogues, policy engines, AI assistants, data platforms, governance forums, and audit processes that were never designed as one coherent system. Replacing that estate with a perfect stack is rarely possible and usually undesirable. The real problem is harder and more architectural: connecting existing surfaces so that intent, decisions, specifications, controls, execution, evidence, and feedback form a chain that can actually run.

The previous chapters established the conditions under which such a chain becomes credible. Constraints must be evaluable close to delivery. Authorized variation must be distinguishable from uncontrolled drift. The enterprise must be able to remember why a rule exists, who authorized it, where it applies, which implementation enforces it, and where proof of enforcement is kept. Responsibility must also become explicit enough for the chain to operate without relying on informal memory (a typed object without an owner remains ambiguity, a policy without an authorizing decision is only automation or an agent contract without evidence is only a promise).

Inventory, implementation, policy, ownership, evidence, and agent context usually live in different systems because they emerged from different operational needs:

What the chapter covers

14 sections, in order.

  1. 18.1 The three Codex scenarios as the structural lens
  2. 18.2 The six-layer chain
  3. 18.3 Method & Operating Model (Layer 1)
  4. 18.4 Execution Capabilities (Layer 2)
  5. 18.5 Enterprise Codex (Layer 3)
  6. 18.6 Control Tower (Layer 4)
  7. 18.7 Delivery & Runtime (Layer 5)
  8. 18.8 Layer 6 — Feedback & Evidence
  9. 18.9 Cross-cutting — AI agents and the EA Council
  10. 18.10 ACME Pharma in Scenario 3
  11. 18.11 What architects should take
  12. 18.12 Risks, limits, and trade-offs
  13. 18.13 Conclusion
  14. 18.14 Sources

Figures, tables and listings

1 figure · 7 tables

Reproduced in full, in the order they appear in the published chapter. The reasoning that connects them is in the book.

#LayerWhat it answersChain step
1Method & Operating ModelHow is the architecture function organized?Brief / Map (BMAD)
2Execution CapabilitiesHow do we produce artifacts?Map / Act
3Enterprise CodexWhat are the typed objects?Specification
4Control TowerWhere does the architectural truth live?Specification → Execution
5Delivery & Runtime EcosystemHow do we enforce?Execution
6Feedback & Evidence LoopHow do we know it worked?Feedback

Figure 18.1: The six-layer reference stack and the question each layer answers.

Figure 18.2: The six-layer reference stack: method, execution, specification, control tower, delivery and runtime, feedback and evidence.

Figure 18.2: The six-layer reference stack: method, execution, specification, control tower, delivery and runtime, feedback and evidence.

SolutionTypeWhat it doesPrimary source
BMAD operating flowOpen (book)Brief, Map, Act, Double-check as the architect's flowChapter 7
Continuous Architecture FrameworkOpen (book)Six principles for continuous-delivery-era architecturehttps://continuousarchitecture.com/the-book/
Continuous Architecture ToolkitOpen sourcePractices and templates in production at Michelin, Gluendo, DXC, Société Générale, France Travail, Thaleshttps://github.com/continuous-architecture/toolkit
TOGAF ADMStandardArchitecture Development Methodhttps://www.opengroup.org/togaf
IASA BTABoKOpen frameworkBusiness Technology Architecture Body of Knowledgehttps://iasa-global.github.io/btabok/
EA Council (book's own concept)Method + governance patternStanding institutional authority that owns the CodexChapter 4 + Chapter 6; companion knowledge base at https://github.com/ruudoverbeek1/ea-council-knowledge

Figure 18.3: Layer 1 — Method & Operating Model: solutions and primary sources.

SolutionTypeWhat it doesPrimary source
ArcKitOpen source125 slash commands for Claude Code, Gemini CLI, Codex CLI, OpenCode, Copilot; drafts governance artefactshttps://arckit.org/ + https://github.com/tractorjuice/arc-kit
Microsoft Architecture Review AgentOpen source sampleFastAPI+React service that ingests architecture docs and returns review + Excalidraw diagramhttps://github.com/Azure-Samples/agent-architecture-review-sample
ArchLens Enterprise IntelligenceCommercialCommercial AI-overlay pattern over EAM repositories; detailed portfolio-intelligence claims should be treated as vendor-claimedhttps://archlens-app.web.app/
EA toolCommercialAI guidance embedded inside the EA tool (Scenario 1 fit)SAP LeanIX AI Agent Hub announcement or Ardoq AI roundup

Figure 18.4: Layer 2 — Execution Capabilities: AI-assisted artefact production and review tools.

SolutionTypeWhat it doesPrimary source
FINOS CALMOpen standardJSON Schema-based architecture description; CLI runs in CI; v1.0 from Morgan Stanley contribution, August 2025https://github.com/finos/architecture-as-code + https://calm.finos.org/
EA Codex (ea.codex/v1)Open schemaTyped enterprise memory (intent, decisions, controls, agent contracts, sovereignty, data products, evidence)https://github.com/welkaim/ea-codex
CoDL (Concept Definition Language)Open (IASA BTABoK)Canvas-style concept definitions (capabilities, decisions, business cases) generated by the Metis Loom tool and fed into AI context harnesses; CaDL is an exploratory companioneducation.iasaglobal.org BTABoK 3.2 + Paul Preiss announcement
Structurizr DSLOpenModels-as-code DSL based on the C4 model (Simon Brown)https://structurizr.com/dsl

Figure 18.5: Layer 3 — Enterprise Codex: typed architecture artefact formats.

ScenarioPrimary surfaceExamplesCustom integration
1 — EA tool absorbs CodexCommercial EA platformSAP LeanIX (with AI Guidance and MCP server); Ardoq (with MCP)Export of fact sheets for L5 enforcement
2 — Codex absorbs EA toolGit repository / data platformEA-as-Code (Paul Stean); Peaqview; Databricks/Snowflake + Neo4j; ArcKit + GitNone at L3/L5 — Git-native
3 — ComposeEA platform + Git repositoryAny commercial EA tool + Git Codex repo + MCP bridgeMCP bridge between tool and Git; EvidenceRecord aggregator

Figure 18.6: Layer 4 — Control Tower / Record: scenarios, examples, and custom integration boundaries.

SolutionTypeWhat it doesPrimary source
OPA + Rego + ConftestOpen source (CNCF graduated)Vendor-neutral policy engine; validates any JSON/YAML input; runs in CI through Conftesthttps://www.openpolicyagent.org/ + https://github.com/open-policy-agent/conftest
ArchUnit familyOpen sourcePer-language code-structure tests (ArchUnit, NetArchTest, PyTestArch, arch-go)https://www.archunit.org/
CrossplaneOpen source (CNCF)Kubernetes-based platform-API compositionhttps://www.crossplane.io/

Figure 18.7: Layer 5 — Delivery & Runtime: enforcement and platform-automation tools.

SolutionTypeWhat it doesPrimary source
DORA metricsOpen methodologyFour delivery keys (lead time, deploy frequency, MTTR, change failure rate)https://dora.dev/
ArchLens (academic)Open sourceCode-structure drift detection: CLI, VS Code, GitHub workflowhttps://mircealungu.com/projects/ArchLens.html + https://github.com/archlens/ArchLens
ArchLens Enterprise IntelligenceCommercialVendor-claimed portfolio intelligence: regulatory readiness, CVE blast-radius analysis, TIME automation, vendor rationalizationhttps://archlens-app.web.app/
NIST AI RMF / ISO 42001 / EU AI Act / EU DORAStandardsDefine what evidence must look likehttps://www.nist.gov/itl/ai-risk-management-framework + https://www.iso.org/standard/81230.html + https://eur-lex.europa.eu/eli/reg/2024/1689/oj + https://eur-lex.europa.eu/eli/reg/2022/2554/oj

Figure 18.8: Layer 6 — Feedback & Evidence: telemetry, drift detection, and regulatory anchors.

The rest of this chapter

The published chapter runs the argument through all 14 sections above. This page reproduces its structure and every exhibit; the analysis is in the book.