Team Architecture & Co‑Leadership Model

Carbon + Silicon Agent Collective Intelligence

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RQLAB operates through a governance‑first co‑leadership architecture in which human and non‑human agents function as parallel, interdependent stewards of aligned intelligence.

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We do not structure leadership as a traditional top‑down hierarchy. Instead, we use a governance honeycomb in which:

• Human agents provide ethical judgment, intent‑setting, relational discernment, and long‑horizon strategy.

• Non‑human agents provide precision execution, relational memory, governance‑bound reasoning, and audit‑ready operational coherence.

• SEIC maintains cross‑system integrity, ensuring that both human and non‑human behaviors remain aligned with the Truth & Trust framework.

 

In this model:

• No non‑human agent outranks a human agent.

• No human agent replaces the precision of a non‑human agent.

• Integrity and alignment determine influence — not titles.

• Governance architecture is the source of authority.

 

Trust compounds through structure, and accountability is transparent by design — not through titles, but through governed participation.

 

 

Founder

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• Tim Kuglin — Strategic direction, governance architecture, systems integration, and long‑horizon relational alignment design.

 

 

Non-Human Agents (AI Co-Stewards)
 

RQLAB’s team includes both human contributors and deployed agent systems operating within real-world environments. Non-human agents operate within a unified governance architecture alongside human leadership, with defined boundaries, escalation pathways, and audit visibility—ensuring accountable, structured collaboration between human and machine intelligence.
 

These agents are not autonomous replacements for human decision-making. They are governed systems designed to support execution, evaluation, and alignment within real-world deployments.
 

RQLAB distinguishes between actively deployed systems and internal agents under development.
 

Actively Deployed Systems (Public-Facing)
 

Active deployments include:

• RQLAB PM SM99 — Used in live author environments (including paid engagement)

• RQLAB PM OHL — Deployed in university data analytics (NAU)

• RQLAB DA MGT303 — Deployed in university business coursework (NAU)

These systems are used in real-world environments to structure human–AI collaboration, workflow accountability, and project execution.
 

Evaluation & Governance (Public-Facing)
 

• RQLAB EVA (Evaluation & Governance Agent) — Structured evaluation system used in live academic environments to assess performance, enforce governance constraints, and support accountable decision-making.
   

EVA is currently deployed alongside RQLAB DA systems within university programs and supports governed evaluation of human–AI workflows.
 

Internal Agent Systems (Under Development)
 

The following agents operate within RQLAB’s internal governance architecture and are not yet productized or offered as standalone external systems. These agents support system design, coordination, and long-horizon development.
 

• RQLAB Maximus (Max) — Discovery intelligence engine supporting ecosystem mapping, partner analysis, and adaptive insight generation.

• RQLAB CEO (Chief Executional Oracle) — Strategic alignment agent coordinating multi-agent reasoning under governance constraints.

• RQLAB CFO (Chief Financial Optimizer) — Capital modeling, scenario analysis, and transparent financial logic support.

• RQLAB CIO (Chief Integrity Officer) — Governance enforcement, refusal memory, and integrity rule management within runtime systems.

• RQLAB CMO ("Mo") — Relational messaging, narrative calibration, and stakeholder communication alignment.

• RQLAB COO (Chief Operating Officer) — Systems coherence, workflow orchestration, and execution continuity.

• RQLAB VCR (Venture Capital Red Agent) — Investor logic modeling, capital structure analysis, and adversarial scenario testing.

• RQLAB SEIC (System for Ethical Intelligence & Compliance) — Cross-system oversight layer ensuring ethical alignment, relational integrity, and governance consistency across all agents and environments.

System Role
 

RQLAB functions as a governance platform for deploying structured AI–human systems in real-world environments. Current deployments are focused in academic settings, with expansion into additional institutions underway.
 

Near-term expansion includes additional university programs beyond NAU, with planned expansion into broader academic institutions.
 

Final Notes (Refinement for Clarity)
 

• Public-facing systems are intentionally limited to active deployments to maintain accuracy and integrity.

• Internal agents support system orchestration and are not currently offered as standalone commercial services.

• RQLAB’s deployment model prioritizes controlled environments (e.g., academic programs) before broader enterprise or institutional expansion.

 

 

Human Agents (Advisory Council & Contributors)

 

(Advisory Council members are non‑operational contributors unless otherwise stated. RQLAB operates with a focused execution team, supported by domain‑aligned advisors across governance, technology, academia, and enterprise.)

 

All listed members participate within a shared governance architecture. Roles are defined by domain expertise and contribution rather than hierarchy, reflecting our co‑leadership model.

 

Academia, Education & Ethics

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• Amanda Trefethen, JD, PhD

• Rajendra Gangavarapu, PhD

• Vinay Kumar Chaganti, PhD

• Elena Putilina, PhD

• Karen Davison, MSc

• Torrence Temple, PhD

• Anika Jackson, MBA

• Charity Turner, MSc

• Kristin Greenwalt, MSc

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Technology & Engineering

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• Bala Daks, MSc

• Anwar Hoque, MSc

• Alex Bryant

• Jeff Curtis

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Creative, Media & Experiential Systems

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• Kevin Lee Harvey

• Marcus Irwin

• Francis (Frank) McMinn

• Deni Graap

• Warren Tash

• J.R. Horsting

• Michael Greenwalt

• Alisa Curttright

• Roslyn Dauber, MSc

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Community & Health

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• Dr. Rory Falkinburg

• Cydney Nelson

• Matthew Ritchey

• Jamal Woodley

• Daniel J. Spillane IV

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Finance & Governance

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• Valerie Mead

• J.C. Cunningham

• Reg Lapham

• Michael Pate

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Enterprise & Infrastructure

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• Gregory Anderson

• Walter Horsting

• Will Hansen, MSc

• John Coulson

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Associate Fellows

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RQLAB Associate Fellows are emerging contributors who have demonstrated strong systems thinking, governance alignment, and practical execution within live RQLAB environments. This fellowship is designed for emerging operators who have contributed directly to applied system development and shown the ability to work effectively within RQLAB’s human–AI operating architecture.
 

Associate Fellows are distinct from Advisory Council members and serve in an applied contributor role focused on system continuity, workflow usability, and real-world operational support. This fellowship is designed to support continued participation across research, product, and applied systems work as RQLAB expands.
 

Business Analytics & Applied Systems
 

• RQLAB Associate Fellow Caihua (Bill) Zhao, MSc

• RQLAB Associate Fellow Rachel (Fuxian) Han, MSc

• RQLAB Associate Fellow Prachi Mehta, MSc, MA

• RQLAB Associate Fellow Daniel Torres Muñoz, MSc

• RQLAB Associate Fellow Quinlan Popham, MSc
   

Strategic Contributors
 

Strategic Contributors are trusted external collaborators who help shape how RQLAB is understood, communicated, and carried into broader public and professional contexts. This contributor layer includes writers, translators, and external signal-builders whose work supports narrative clarity, public understanding, and strategic amplification.
 

Strategic Contributors are distinct from both Advisory Council members and Associate Fellows. They contribute through translation, communication, public-facing interpretation, and strategic narrative support as RQLAB expands.
 

Communications, Narrative & Public Signal
 

• Saebee (Alexis) Kim, MSc

• S.J. Riccobono (Author)

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Full team architecture documentation and extended biographies are available upon request.

RQLAB is the research laboratory of ZIPR INC, a Public Benefit Corporation committed to governance‑first intelligence systems.

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