---
title: "The AI-Interrogable Business Enterprise Architecture"
subtitle: "A staged path from architectural documentation to enterprise decision support, grounded in a working prototype and the GAO-26-107663 mandate"
author: "Dr. Nate Dailey"
date: "June 2026"
abstract: |
  Traditional enterprise architecture answers one question: what exists. The Department of Defense Business Enterprise Architecture (BEA) is a well-structured, statutorily mandated model that nonetheless sits largely inert as documentation. This paper argues that the BEA can become AI-interrogable, that the lift to get there is operational rather than architectural, and that a working prototype already demonstrates the core pattern end to end. It presents the Autonomous Enterprise Decision Architecture (AEDA), a layered reference design that carries an enterprise from ontology through knowledge graph, graph-grounded retrieval, agentic reasoning, and eventually toward continuous decision support. It stages that design honestly against what runs today, what a focused proof-of-concept will deliver by November 2026, and what remains a multi-year ambition. Every near-term claim is anchored to a specific recommendation in GAO-26-107663 and to a named component of the proven NASA Future-State round-trip pipeline.
format:
  docx:
    toc: true
    toc-depth: 2
    number-sections: true
---

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# Executive Summary

The Department of Defense invests years in building enterprise architectures that, once published, behave like reference books. They describe what exists. They do not answer the questions leaders actually ask: which systems deliver a given capability, where capabilities are duplicated across components, what breaks if a system is modernized or retired, and whether a proposed capability already exists somewhere in the portfolio. Those four questions are the daily work of the Defense Business Council, and today they are answered by manual research across static documents, if they are answered at all.

The MyTravel abandonment is the cost of that gap. GAO-26-107663 traces the failure to four root causes: absence of a central leadership authority, insufficient program management practice, inadequate stakeholder outreach, and inconsistent user inclusion. Each of those is structurally detectable in a machine-readable architecture before it compounds into a systemic failure. The thirteen recommendations in that report are, read together, a specification for an architecture that can be queried, traced, and held to account.

This paper makes three claims.

First, the BEA can be made AI-interrogable, and the lift is operational rather than architectural. A working prototype at MITRE already executes the full pattern on a different model: an architecture built deterministically from a curated corpus, exported through an open interchange format, parsed without proprietary tooling, and exposed to AI consumers through a governed Model Context Protocol (MCP) server with six tools. The same pattern, applied to BEA artifacts, produces the natural-language interrogation surface the sponsor needs.

Second, this near-term capability is the first rung of a longer design, the Autonomous Enterprise Decision Architecture (AEDA). AEDA stages an enterprise from ontology discovery through knowledge graph, graph-grounded retrieval, and agentic reasoning, toward digital twin, simulation, optimization, and continuous decision support. This paper presents the full design but is deliberate about maturity. It marks clearly what runs today, what a focused proof-of-concept will deliver by November 2026, and what remains a multi-year ambition not funded in this cycle. Honesty about staging is what separates a fundable vision from a vapor one.

Third, bidirectional traceability is the crown jewel and the strongest single justification for the program. It is what a typed knowledge graph with source provenance delivers natively and what static document repositories cannot deliver at all. It satisfies GAO-26-107663 Recommendation 13 by construction.

The proof-of-concept is achievable on the committed schedule and funding. The evidence base is a corpus of twenty-three systematic literature reviews, each anchored to a GAO recommendation and to the working baseline. The recommendation of this paper is to fund the four-phase proof-of-concept that ends at graph-grounded natural-language interrogation, to defer the autonomous-enterprise layers to a later cycle, and to treat the BEA proof as the template for cross-domain expansion in fiscal year 2027.

# The Problem: Architecture as Documentation

The BEA is in good shape. It is well-structured, statutorily anchored under 10 U.S.C. section 2222(e), and backed by accessible digital data. That is precisely what makes it the right starting point. The problem is not the quality of the model. The problem is that a high-quality model expressed as documentation cannot answer questions, cannot trace a requirement backward to its origin or forward to its dependents, and cannot surface a duplication or a single point of failure without a human reading every page.

GAO-26-107663 is the immediate forcing function. Its thirteen recommendations span documentation of Agile practice, codified leadership authority, substantiated compliance, integrated milestones, performance metrics linked to goals, non-functional requirements, stakeholder value scoring, council leadership, dynamic outcome reassessment, traceability metrics, repeatable testing, stakeholder requirement adjustment, and bidirectional traceability of requirements. The Defense Travel System is currently the only modernization initiative classified to Congress as a high-risk priority business system through the Defense Business Council.

The MyTravel post-mortem is the counterfactual that frames the entire program. An AI-interrogable BEA would have surfaced the four root-cause failure factors structurally, through architectural signals, before more than twenty million dollars was lost: under-allocated capabilities, redundancy growth, eroded interfaces, and the absence of a single accountable authority encoded anywhere in the operating model. The recurring theme of this paper is that those failures are detectable in advance when the architecture is machine-readable and queryable, and invisible until too late when it is not.

# The Wedge: An AI-Interrogable BEA on a Proven Baseline

The fastest credible path to value is a narrow one. Rather than attempt a department-wide transformation, the proof-of-concept makes one model answerable in plain English and uses that to discharge the GAO recommendations that matter most.

This is not a green-field build. A working baseline already runs. The NASA Future-State round-trip pipeline at MITRE comprises five components that together form the pattern the BEA prototype extends:

- An Archi 5.8 model with 51 elements, 46 relationships, and three governed views.
- An AI-generated jArchi automation script that builds the model deterministically from a curated corpus, applying MITRE brand colors and semantic coding. The script is the iteration unit; the model is the deliverable.
- An ArchiMate Open Exchange XML interchange so the artifact ingests into any DoDAF-aligned or DM2-aligned tool without a licensed-plugin dependency on the consumer side.
- A standalone Node.js parser that emits JSON, Markdown, CSV, and Mermaid outputs without requiring the Archi runtime, so any machine with Node.js can consume the model.
- A Model Context Protocol server exposing six tools (parse_archimate, list_elements, find_relationships, get_element, summarize_model, export_mermaid) that expose the model to AI consumers over a governed contract. Every call records caller identity, tool, parameters, and timestamp.

This pipeline has executed end to end. AI-generated scripts produced the in-tool diagrams, the export round-trips back to AI-consumable form, and the MCP server answers structured queries against the model today. The lift to apply the same pattern to BEA artifacts is operational, not architectural. That distinction is the central de-risking argument of the program: the hard pattern is already proven; what remains is to point it at a new corpus.

# The Architecture: AEDA, Staged Honestly

AEDA is the reference design that the wedge is the first rung of. It is presented here in full, and then staged against maturity so that no layer is oversold.

## The AEDA Layers

The canonical AEDA flow is:

Enterprise data sources, then ontology discovery, then ontology governance, then enterprise ontology, then knowledge graph, then graph-grounded retrieval (GraphRAG), then agentic reasoning, then enterprise digital twin, then simulation, then optimization, then decision support, then executive support.

The thesis is a progression of questions. Traditional enterprise architecture answers "what exists." AEDA is designed to answer, in sequence as the layers mature, "what is happening," "what will happen," "what should we do," "what are the risks," and "what alternative futures exist." The near-term program addresses only the first two of those beyond the baseline. The remainder is the multi-year horizon.

## The Maturity Ribbon

The discipline that keeps this fundable is the explicit separation of three maturity tiers.

**Running today.** The deterministic build pipeline, the open interchange export, the standalone parser, and the six-tool MCP server. The ontology and knowledge-graph primitives exist in working form on the NASA model. Bidirectional relationship traversal is live through find_relationships.

**Proof-of-concept by November 2026.** A BEA-specific canonical schema; multimodal ingestion of real BEA artifacts; a knowledge graph and vector store over those artifacts; a graph-grounded retrieval orchestrator over the existing MCP server; and natural-language answers to the four governance queries with sentence-level citation chains. This is AEDA layers one through six applied to the BEA. It stops at GraphRAG. It is the funded scope.

**Fiscal year 2027 and beyond.** Agentic reasoning at scale, the enterprise digital twin, discrete-event and Monte Carlo simulation, multi-objective and portfolio optimization, and continuous decision support. These layers are real architecture and the subject of active research, but they are not in the proof-of-concept and are not promised on the current schedule. They are the reason the proof-of-concept matters: it is the foundation they require.

## The Crown Jewel: Bidirectional Traceability

Among all thirteen GAO recommendations, Recommendation 13 (bidirectional traceability of requirements) is the strongest single justification for the program, because it is exactly what a typed knowledge graph with source provenance delivers natively and what document repositories cannot deliver at all.

The prototype extends the relationship taxonomy from the NASA model to the BEA across four families: vertical (decomposed-by, realizes, allocated-to, implements), horizontal (depends-on, interfaces-with, produces, consumes, contends-for), compliance (satisfies, violated-by, justified-by, verified-by, proved-through), and temporal (supersedes, superseded-by, affects, affected-by, evolved-from). Cross-view trace-link prediction with graph neural networks [\[1\]](#ref-1) and triple-graph-grammar model synchronization with formally proven correctness [\[2\]](#ref-2) establish that these links can be created and maintained automatically at scale. Traversal from any node in either direction returns a full citation chain to the source artifact, page, and paragraph. Comparable graph-native traceability deployments report substantial reductions in audit cycle time and high consistency verification across multi-year system evolution [\[3\]](#ref-3).

## The Metamodel: A Deliberate Hybrid

A federated department cannot be forced onto a single metamodel, and it does not need to be. The Phase 1 canonical object model anchors on DoDAF DM2 as the statutory mandate under section 2222(e), with federation layers that translate to ArchiMate (proven in the working baseline) and to OMG UAF (forward-positioned for SysML v2). The federation layer provides translation and alignment services rather than forcing components into one schema, a pattern validated by federated virtualized knowledge-graph frameworks operating across heterogeneous sources [\[4\]](#ref-4). This is the pattern the literature supports for federation at federal scale, and it is forward-compatible with the modernized DoDAF deliverables the IE Tiger Team is expected to ship in mid-2026. Retrieval-augmented generation over model-based systems-engineering graphs shows the SysML v2 path is tractable [\[5\]](#ref-5).

# The Evidence Base

The design rests on twenty-three systematic literature reviews, each engineered to a single GAO recommendation and to a named component of the working baseline, and consolidated into a decision-grade synthesis. The reviews converge on eight cross-cutting findings that the prototype treats as continuous threads rather than discrete tasks:

1. Bidirectional traceability is an architectural primitive, not a documentation chore [\[1\]](#ref-1), [\[3\]](#ref-3).
2. The Model Context Protocol is the governed contract through which all AI-augmented operations run [\[6\]](#ref-6), [\[7\]](#ref-7).
3. W3C PROV-O lineage is the right pattern for binding every artifact to its source [\[8\]](#ref-8), [\[9\]](#ref-9).
4. Graph-grounded hybrid retrieval, paired with citation-grounded synthesis, is the production pattern for a natural-language decision surface [\[10\]](#ref-10), [\[11\]](#ref-11), [\[12\]](#ref-12), [\[13\]](#ref-13).
5. Schema evolution must be governed and additive, never monolithic.
6. Non-functional requirements (security, privacy, Zero Trust, compliance) are first-class graph citizens that traverse the same tools as functional architecture [\[14\]](#ref-14), [\[15\]](#ref-15).
7. AI-generated refinement is governed by construction, with verifiable authority records and human-in-the-loop escalation [\[16\]](#ref-16), [\[17\]](#ref-17), [\[18\]](#ref-18).
8. MyTravel-style failure factors are detectable through architectural signals [\[19\]](#ref-19), [\[20\]](#ref-20), [\[21\]](#ref-21).

The corpus also grounds the harder downstream capabilities the later AEDA layers depend on: graph-reinforcement microservice decomposition and autonomous mainframe modernization for legacy disposition [\[22\]](#ref-22), [\[23\]](#ref-23), and compliance-as-code with cognitive context orchestration for the continuous-compliance substrate [\[24\]](#ref-24), [\[25\]](#ref-25).

The synthesis confirms the working baseline as the right starting substrate, confirms the six-phase sequencing, and modifies a small number of design choices rather than contradicting any. The principal modifications are to front-load schema-versioning discipline into Phase 1, to add MCP capability attestation and origin authentication in Phase 3, and to lock quantitative acceptance criteria for the retrieval layer before that layer is built.

# GAO-26-107663 Coverage

Each of the thirteen recommendations is covered by multiple reviews and discharged by a specific prototype mechanism rather than by a memo.

| Rec | Subject | Discharged by |
|----|---------|---------------|
| 1 | Document Agile practices | Architecture-as-code: the build script is the documented practice, the model is the auditable deliverable |
| 2 | Leadership roles | Decision rights encoded in MCP tool authorizations |
| 3 | Substantiate compliance | Compliance evidence as a queryable property returned with every answer |
| 4 | Integrate interim milestones | Program phases as queryable architectural elements with live status |
| 5 | Metrics linked to goals | Strategic-outcome properties on every element, projected by summarize_model |
| 6 | Non-functional requirements | NFRs as first-class schema entities traversing the same tools |
| 7 | Stakeholder value process | Stakeholder-class and score properties encoded in the schema, not a static rubric |
| 8 | Defense Business Council leadership | Council cadence driven by live queries; decisions recorded with provenance |
| 9 | Dynamic outcome reassessment | Governed additive schema with delta projection on a defined cadence |
| 10 | Agile metrics traceability | Metric-to-source chaining through find_relationships |
| 11 | Repeatable testing | Deterministic build and round-trip conformance tests on every ingestion |
| 12 | Stakeholder requirement adjustment | Stakeholder content ingested directly into the graph, not a parallel system |
| 13 | Bidirectional traceability | Typed knowledge graph with source provenance, traversable in either direction |

# Implementation Roadmap

The program is six phases. The proof-of-concept is Phases 0 through 4. Phase 5 is the hand-off. The autonomous-enterprise layers are explicitly out of this scope.

- **Phase 0. Pattern validation.** Demonstrate the working baseline against the NASA model to the sponsor, establishing that the hard pattern is proven before any BEA work begins.
- **Phase 1. Canonical schema bootstrap.** Stand up the deliberate-hybrid BEA schema with the full property scheme, the bidirectional relationship taxonomy, semantic versioning, and the BCA scoring properties. Commit to forward-DoDAF readiness here, not later.
- **Phase 2. Multimodal ingestion.** Ingest real BEA artifacts (PDF, slides, structured exports) deterministically into the graph and vector store, with round-trip conformance gates.
- **Phase 3. Graph-grounded retrieval.** Build the hybrid retrieval orchestrator over the existing MCP server, with locked quantitative acceptance criteria (citation accuracy above ninety percent, atomic fact verification above ninety-five percent, multi-hop F1 above 0.90, sub-two-second latency at the ninety-fifth percentile), MCP hardening, and the critic-engine quality gate.
- **Phase 4. Sponsor demonstration.** Answer the four governance queries live with citation chains, run the MyTravel counterfactual, and have the prototype report on its own milestone health through its own tools.
- **Phase 5. Hand-off.** Wire the CIO assessor toolkit micro-tools through the same MCP contract so the value-scoring work inherits the authority structure and the evidence chain by construction.

# Risk and Governance

The program governs AI participation rather than banning it, because the working baseline already shows that AI-generated architecture artifacts deliver value when bounded by deterministic build discipline. Every AI-augmented operation runs through the MCP server with signed-event provenance, scoped authorization, read-only defaults, and approval-gated elevation.

The literature surfaces risks worth naming explicitly. Protocol-level MCP vulnerabilities (absence of capability attestation, origin authentication, and implicit trust propagation) are addressed by Phase 3 hardening. Tool-poisoning exposure in MCP ecosystems is mitigated by single-responsibility tool design and pre-deployment validation. Critic-engine adversarial weakness is mitigated by a perturbation-and-edge-case calibration battery with a reviewer-override audit log. Schema-evolution debt is mitigated by the Phase 1 commitment to semantic versioning, dual-tenancy intervals, and a twelve-month minimum deprecation window. Citation-grounding failure under multi-hop synthesis is mitigated by triplet-level graph alignment and quote-level citations with confidence-calibrated escalation.

No risk in the register is a Phase 0 blocker. The central discipline, repeated across the corpus, is to treat the canonical schema as governed and additive from day one.

# Beyond the BEA

The architecture is domain-general by construction. The same progression, from enterprise ontology through knowledge graph, graph-grounded retrieval, and agentic reasoning, applies to any domain with a structured model and a corpus of evidence. The proof-of-concept on the BEA is therefore not only a discharge of the GAO mandate; it is the template for cross-domain expansion. The first and most natural extension, a space-domain decision architecture built on the same stack, is the subject of a separate forthcoming paper and is not in the scope of this program.

# Conclusion

The BEA can be made answerable. The pattern is proven, the evidence base is built, the schedule is achievable, and the strongest justification, bidirectional traceability, is delivered by the architecture rather than by procedure. The recommendation is to fund the four-phase proof-of-concept through graph-grounded natural-language interrogation, to keep the autonomous-enterprise layers in the vision where they belong rather than in the build where they would sink the schedule, and to treat the BEA proof as the foundation for everything that follows.

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