I design and evaluate AI systems with a focus on multi-agent orchestration, prompt systems, evaluation methodology, provenance, governance, reproducible experimentation, and trustworthy deployment controls.

My primary body of work is DGAF (Dynamic Governance Agentic Formation), an evolving research and implementation framework for governed agentic systems. The associated PDMAL (Phi-Driven Multi-Agent Lattice) track investigates multi-agent topology and control experimentally.

DGAF combines agent orchestration, governance gates, evidence handling, provenance, semantic controls, evaluation, and deployment safeguards into a governed control-plane architecture.

Recent engineering work includes a governed recursive control-plane with bounded recursion, immutable governance envelopes, exact state identity and cycle detection, append-oriented provenance, explicit proposal/authorization/commit barriers, capability-boundary tests, hardened lifecycle semantics, audit sealing, exact-head CI checks, source-SHA attestation, and candidate-bound evidence reconciliation.

The current engineering cycle is extending that foundation into a fail-closed confidential-execution path for blinded experimental work. This work covers authenticated attestation verification, explicit pre-execution authorization consumption, in-process key generation, blinded output/manifest binding, post-execution attestation, crash/retry controls, and end-to-end lineage verification. These controls are being developed and tested before any empirical experiment is authorized.

PDMAL is the associated experimental research track. It is designed around deterministic execution, topology generation, blinded comparisons, artifact schemas, provenance, reproducibility, and pre-specified statistical analysis.

Current experimental state: Epoch 001 is retained historical blinded collection evidence (50 paired seed units / 2,250 observations) whose protected mapping is cryptographically unrecoverable; its primary analysis is therefore UNANALYZABLE / NOT RUN. The prospective successor Track A is PRE-FREEZE / FAIL-CLOSED / NOT AUTHORIZED / N=0. Canonical DGAF/PDMAL efficacy is NOT ESTABLISHED. Passing CI, synthetic/dry-run evidence, deployment readiness, or historical verification is not treated as experimental efficacy.

• AI orchestration and multi-agent systems
• Prompt engineering and prompt evaluation
• AI evaluation, QA, and benchmark methodology
• Governance, provenance, and evidence architecture
• Agent control-plane design
• Runtime and deployment verification
• Adversarial testing and failure-mode discovery
• Reproducible experimental infrastructure
• Semantic and ontological boundary governance
• Spatial and acoustic intelligence research

A central design principle of the work is to keep these states distinct:

defined → implemented → computed → verified → attested → historical → hypothesis → unsupported

A passing test, deployment, historical attestation, or repository claim is not automatically equivalent to independent verification or empirical efficacy. Exact candidate identity, execution context, run evidence, provenance, and scope matter.

The project deliberately records unresolved conditions rather than converting implementation maturity into success claims. Historical evidence is retained as provenance and is not silently transferred across candidate, deployment, run, or artifact boundaries.

NDR AI Systems
├── Orchestration
│   ├── DGAF / PDMAL
│   ├── Orbit-Driftwatch
│   ├── Morse-Orchestration
│   └── Agent Control Plane
│
├── Evaluation
│   ├── Driftwatch
│   ├── ResumeApex
│   └── Prompt-system evaluation specifications
│
├── Governance
│   ├── Sentinel Governance
│   ├── Evidence / provenance controls
│   ├── Semantic boundaries
│   └── Runtime / deployment verification
│
└── Spatial / Experimental Interfaces
    ├── ASIS / Acoustic-Mesh
    ├── MeshSense
    └── Phi-Calculus

The portfolio now contains several executable verification surfaces rather than architecture documents alone:

• DGAF has a broad exact-head CI/governance surface. Epoch 001 is historical retained collection evidence whose primary analysis is unanalyzable; the successor track remains PRE-FREEZE / NOT AUTHORIZED / N=0 and efficacy is not established.
• Orbit-Driftwatch has deterministic orchestration, a server-backed provider boundary, source-bound web retrieval, explicit unsupported/conflicting-claim handling, and portable run artifacts; live hosted execution and public deployment are still open.
• Driftwatch has a hardened build/dependency lane plus a retained synthetic detector benchmark, without promoting that synthetic result to real-world efficacy.
• Morse-Orchestration has a validated seeded synthetic harness and paired research methodology.
• Sentinel Governance has a fail-closed repair-mutation boundary and current dependency/validation checks.
• ResumeApex has an executable deterministic evaluation harness with known-answer controls and a green current-main verification workflow.

The highest-value remaining work is increasingly integration and external validation rather than basic implementation: public Orbit deployment, a real provenance-retained hosted Orbit run, DGAF candidate/freeze/authorization progression, representative real-world Driftwatch calibration data, and independently reviewed evidence at the gates that require it.

• Build the apparatus before claiming the result.
• Preserve exact source and evidence identity.
• Fail closed when required evidence is absent.
• Treat adversarial findings as development signals, not embarrassments to hide.
• Keep internal/private working records separate from public claims.
• Distinguish governance-framework resemblance from legal or regulatory compliance.
• Keep commercial, certification, and endorsement claims scoped to explicit evidence.

My current professional focus is AI systems design, AI orchestration, evaluation, prompt engineering, governance, and trustworthy AI infrastructure, with particular interest in roles involving AI evaluation, model quality, prompt systems, agentic workflows, QA, reproducible experimentation, and governance-aware AI engineering.

I am especially interested in work where careful evaluation, evidence quality, provenance, and system-level reasoning matter as much as producing a working prototype.

Public profile last reconciled: 2026-09-09. Project status is maintained in each repository's authoritative evidence and governance records; this profile is a public representation, not an experimental source of truth.