TNQ Research is the standing analytical practice at TrueNorth Quantum. We publish a monthly catalog of reports, methodology references, field memos, and position papers — sourced from active engagements, drawn from the operating ledger of the Northern Shield platform, and intended for technical, regulatory, and executive audiences operating where AI agents now do consequential work.
Featured release · July 2026
POV-2026-001Perspective · Design vs retrofit2026-07-229 pp · PDF
The Governance Lesson We Already Learned: why AI agents will need the same governance discipline enterprise IT eventually got.
A perspective arguing that AI-agent governance is not a new problem. It is the discipline enterprise IT spent four decades learning through incident — identity and least privilege, segregation of duties and change management, tamper-evident audit and blast-radius containment, reversibility and risk classification. The paper maps eight IT disciplines onto their AI-agent equivalents and makes the case that governance designed in at the start costs less, in every measurable dimension, than governance bolted on after the first loss.
Authored by TNQ ResearchClassification · Release 1.0 · Public
Document spine
Section 1 — We have seen this movie
Section 2 — What "ungoverned" looked like the first time
Section 3 — The disciplines IT earned — and the failures that taught them
Section 4 — "But AI is different"
Section 5 — Why this time is faster, and the stakes are higher
A perspective arguing that AI-agent governance is not a new problem — it is the discipline enterprise IT spent four decades learning through incident. Maps eight IT disciplines (identity, least privilege, segregation of duties, change management, tamper-evident audit, blast-radius containment, reversibility, risk classification) onto their AI-agent equivalents, and makes the case that governance designed in at the start costs less than governance bolted on after the first loss.
A perspective on the eight properties that distinguish an audit log from insurance-grade evidence — attributability, tamper-evidence, completeness, contemporaneity, decision provenance, boundedness, reproducibility, and independent verifiability — and why the ISO January 2026 endorsements make this the operating standard whether you are buying cover or not.
An architectural reference for parallel-trained oversight in the regulated enterprise. Surveys the regulatory geography — EU AI Act, SEC enforcement, FDA, banking model-risk, NAIC — and the five-layer full-stack alternative to AI-alone deployment.
A complete enumeration of the engagement artifacts that constitute a TNQ Digital Employee build — phases 1 through 6, with sample structure for each artifact and acceptance criteria for the five sign-off milestones.
TNQ Research18 pp · PDF
MEM-2026-001
2026-05-08
Field memo · Engagement notes
Multi-track commission administration at infrastructure scale
Sanitised for release
Engagement notes from the deployment of a Digital Employee administering three parallel commission tracks across more than 100 active infrastructure projects, including a ten-year declining offtake stream with continuous reallocation.
TNQ Research12 pp · PDF
Coming soon
RPT-2026-002
2026-04-02
Report · Classification framework
The Risk Tier Framework: a five-dimension rubric for agent deployments
Release 1.0
A formal classification rubric for Digital Employee deployments across five risk dimensions — blast radius, reversibility, data sensitivity, regulatory exposure, external visibility — with calibrated control sets and oversight cadences for each of four resulting tiers.
TNQ Research22 pp · PDF
Coming soon
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