Governance you can inspect, not just promise.
I build tests, audit records, and human-approval controls that help teams detect when AI agents exceed their authority—and show reviewers what happened.
Target roles: AI Evaluation Engineer, AI Governance Engineer, Safety Research Engineer, and technical responsible-AI program leadership. Also open to select consulting and research collaboration.
What I can do for your team
Translate broad responsible-AI goals into controls and evidence that technical, governance, and leadership reviewers can inspect.
Design agent-boundary evaluations
Turn authority, escalation, and refusal requirements into structured cases, deterministic checks, and reviewable reports.
Make decisions reconstructable
Create action receipts and audit patterns that preserve what was proposed, permitted, blocked, reviewed, and executed.
Build human-approval workflows
Define when automation may proceed, when it must stop, and what evidence a person needs to make the decision.
Package evidence for review
Connect claims to tests, artifacts, limitations, and reproducible instructions for technical and nontechnical audiences.
Public projects, concrete outputs
Each project identifies the problem, deliverable, and currently public evidence. Repository links open GitHub.
AI Governance Benchmarks
Tests whether documented synthetic agent actions remain inside defined boundaries.
Agent Action Audit
Makes agent decisions easier to reconstruct after an action is proposed or taken.
Human–AI Governance Lab
Shows how risk classification and approval gates can preserve human authority.
Latest work
Dated updates, ordered newest first, with evidence boundaries attached.
From agent-risk question to reproducible evaluation
How a broad governance concern became a bounded scorer, test cases, CI checks, and explicit limitations.
Read the case study →12 July 2026 · Evidence registerClaims mapped to artifacts
A current map of what the public work demonstrates, how to reproduce it, and where the evidence stops.
Review the evidence →12 July 2026 · White paperSovereign AI
A bounded paper on distributed cognition, local infrastructure, data sovereignty, and unresolved limitations.
Read the paper →Looking for someone who can turn AI-governance principles into inspectable evaluation and review mechanisms?
Discuss a role or projectWhy Tom
I bring operational accountability into AI governance engineering.
More than a decade leading operations in high-growth organizations taught me that controls must work under real deadlines, budgets, handoffs, and executive scrutiny. That experience now shapes how I build AI evaluation and oversight artifacts: clear authority, explicit escalation, reconstructable decisions, and honest limits.
OPERATIONS LEADERSHIP → TESTABLE AI GOVERNANCE → REVIEWABLE EVIDENCE
How I work
A system that works but isn't right isn't a system you can trust. I hold every piece of work to three questions at once.
Is it right?
Moral intent is an engineering constraint, not a policy layer added after shipping. Boundaries and refusals are evaluated as behavior, not described in a document.
Is it coherent?
The patterns worth trusting show up across disciplines — cognitive science, cryptography, governance, systems design. When the same structure recurs, that's the signal.
Does it hold?
Every claim should be reconstructable — traceable to a file, a test, or a report. If an artifact can't survive its own review, it doesn't ship.
UNA — governed private research
My private research uses a rule-based governance layer: proposed changes pass through review checkpoints, and governed changes produce records that can be audited later.
In plain language: the system separates proposing an action from approving and executing it. The public artifacts are clean-room examples of that method; they do not independently verify the complete private system.
The site explains the work. GitHub shows the artifacts.
Public repositories, benchmark docs, reproducible runners, CI evidence, and honest limitations — inspectable support for the bounded claims made here.