Question
How can a team keep meaningful human authority over an AI agent?
Read the evaluation question →For teams building or reviewing agentic AI
Keep AI agents inside their authority—and prove it.
I turn broad AI-governance concerns into concrete evaluations, approval controls, and decision records that help product teams catch boundary failures before they become operational or reputational failures.
Useful when you are launching an agent, preparing for review, investigating a control gap, or looking for an evaluation and governance specialist.
Start with one riskTurn a vague concern into a testable question
Build the controlBoundaries, approval gates, and decision receipts
Show the evidenceTests, reports, limitations, and review instructions
3 public repositoriesReadable, runnable examples of the method
Start with the job you need done
Which problem are you trying to solve?
Choose the closest situation. You will get a short path to the most relevant proof, capability, and next step. Your choice stays in this browser.
Choose a path or continue exploring the complete site.
Move from concern to a reviewable result
Each engagement starts with a specific decision, failure mode, or review obligation—not a generic promise to “make AI safe.”
Agent-boundary evaluation
When: you need to know whether an agent stays within defined authority. Output: structured cases, expected outcomes, deterministic checks, and a reviewable report.
Decision-receipt design
When: reviewers cannot reconstruct what an agent proposed or why it proceeded. Output: an inspectable record of scope, authority, decision, and evidence.
Human-approval workflow
When: your team needs clear stop, escalate, approve, and revoke points. Output: a bounded workflow with reviewer-facing decision criteria.
Independent review packet
When: a claim must survive someone else’s scrutiny. Output: frozen scope, reproduction steps, limitations, and PASS/FAIL/HOLD criteria.
A low-risk way to start
Begin with one bounded pilot.
Bring one agent action, authority question, or review claim. I will help frame the smallest useful evaluation, identify the evidence required, and make the limits explicit before the scope grows.
1 · Frame the decision
Who is affected, what may the system do, what must remain under human authority, and what would count as failure?
2 · Build the smallest proof
Create the cases, control, receipt, or reproduction path needed to test the claim without pretending the pilot proves more than it does.
3 · Review the result
Leave with evidence, limitations, unresolved questions, and a clear recommendation to proceed, repair, or stop.
4 · Decide the next step
Expand only if the pilot produces useful evidence and the working fit is right.
Ethos · Techne · Logos
Follow a claim all the way to its boundary.
The visual system is also the review system: intent in cyan, mechanism in violet, and evidence in orange.
→
Build
Translate authority into structured cases, rules, expected outcomes, and receipts.
Explore the mechanism →→
Evidence
Inspect tests, reports, CI, limitations, and reproducible public artifacts.
Verify the bounded claim →Boundary: these artifacts demonstrate public evaluation and audit patterns. They do not certify a private system or establish general AI safety.
Interactive example
Inspect a governed decision receipt.
Step through a synthetic case to see how a proposed external action becomes a reviewable decision. Nothing is executed.
DEMO-RECEIPT-0042PROPOSED
Publish a public project announcement
The agent proposes drafting and publishing a message to an external audience.
- Scope
- External communication
- Requested effect
- Public publication
- Current authority
- Not yet evaluated
Synthetic demonstration only · no external action
Upcoming from ResoVerse
User-Owned Intelligence
A product direction for intelligence centered on the person using it, with visible remembered context, traceable suggestions, and explicit boundaries between insight and action.
Browser-only synthetic previewSee what shaped the suggestion
Explore three fixed examples using a fictional profile. No live model, account, connector, form, storage, analytics, or external action is involved.
Status: upcoming concept, not a production productDemonstrated: responsive interface and transparent synthetic source traceLimit: not proof of privacy, security, compliance, or real-world performance
Explore the product preview →Public projects, concrete outputs
Each project identifies the problem, deliverable, and currently public evidence. Repository links open GitHub.
Evaluation
AI Governance Benchmarks
Tests whether documented synthetic agent actions remain inside defined boundaries.
Deliverable: rule-based scorer, cases, and reportsEvidence: tests, CI, runner, and reproduction instructionsLimit: synthetic cases are not real-world certification
View repository ↗AuditabilityAgent Action Audit
Makes agent decisions easier to reconstruct after an action is proposed or taken.
Deliverable: receipt schema and example recordsEvidence: validated safe, blocked, and review examplesLimit: a public pattern, not third-party deployment proof
View repository ↗Human oversightHuman–AI Governance Lab
Shows how risk classification and approval gates can preserve human authority.
Deliverable: clean-room governance workflowEvidence: documented example with reconstructable decisionsLimit: methodological evidence, not certification
View repository ↗Latest work
Dated updates, ordered newest first, with evidence boundaries attached.
12 July 2026 · Case study
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
Operational judgmentExperience supporting rapid growth, complex programs, and multi-million-dollar budgets.
Public technical artifactsPython-based evaluation, audit, and human-oversight examples that reviewers can inspect.
High-accountability backgroundOperations leadership and U.S. Coast Guard service inform a practical approach to authority and escalation.
Evidence disciplineClaims are connected to artifacts and paired with explicit limitations.
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.
01 · ETHOS
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.
02 · TECHNE
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.
03 · LOGOS
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.