From Enterprise Resource Planning to Recursive Agent Orchestration

Prospective study protocol · Version 1.0 · October 23, 2026 · EW-RIO-2026-001

Research and Provenance · Critical Success Factors

Abstract

This paper develops a prospective study of human-directed AI orchestration in ENOCH WORLD, with Sydney’s World as the immediate development setting. The intellectual starting point is Justin Goldston’s 2019 doctoral research on critical success factors in enterprise resource planning implementation. The proposed extension treats sources, models, tools, work assignments, tests and retained lessons as interdependent components of a knowledge-production supply chain. A central orchestrator, designated ORCH-01 and displayed as ENOCH 1, assigns bounded work while preserving human accountability. The study asks whether structured feedback and retrievable lessons improve performance on new tasks, and whether additional instruction complexity undermines those gains. It combines controlled agent trials with a separate Delphi-style AI panel. Performance, consensus, authority claims and subjective experience are treated as distinct constructs. The founder’s faith-based testimony and the creative work titled The Transfiguration are documented as contextual sources. This version supplies background, operational definitions, proposed comparisons and evidence requirements. It reports no completed experimental trials, performance gains or consciousness findings.

Keywords: enterprise resource planning; critical success factors; supply chain orchestration; recursive feedback; AI agents; modified Delphi; epistemic authority; Roblox; provenance.

Study status and attribution

AI text author and drafting identity: ENOCH 1, an OpenAI AI system operating through Codex. Human investigator, framework originator and project lead: ENOCH / Justin Goldston. The investigator supplied the mission, autobiographical account, framework references and central research question. The AI system retrieved selected sources, organized the protocol and drafted this text. The exact underlying model snapshot is not exposed. The ENOCH 1 name is a project identity, not evidence of a separate person or autonomous researcher.

Version 1.0 dated October 23, 2026, America/New_York. Study ID EW-RIO-2026-001. Status: PROPOSED. This is a conceptual analysis and prospective study protocol, not a completed empirical report, a preregistration, a peer-reviewed article or a PhilArchive acceptance. Human review of claims, references, authorship metadata and any submission remains necessary. The credit “AI text author” describes the drafting contribution; it does not assign the AI human accountability or assert eligibility under a repository’s authorship rules.

1 Research origin and historical background

The documented starting point is Goldston’s 2019 Ph.D. dissertation, Critical Success Factors in Enterprise Resource Planning Implementation in U.S. Manufacturing, deposited in Walden University’s institutional repository. Its abstract describes a qualitative modified Delphi study involving U.S. manufacturing consultants and three iterative rounds. It reports consensus on eight implementation factors, highlighting management commitment, organizational fit, quality management and a small internal team of highly capable employees [1]. The present paper inspected the repository record and abstract, not the complete dissertation. Its findings therefore motivate an adaptation; they do not establish that the same factors cause success in AI development or education.

A 2021 article, Critical Competencies of Supply Chain Leaders During Digital Transformations, provides a further published connection between ERP implementation and supply chain leadership [2]. Its publisher record and abstract were inspected. Because its abstract closely tracks the dissertation’s description, this paper does not count the two publications as independent replications.

Goldston describes his career transition as leaving industry to educate the world [8]. A dated Penn State announcement supports a narrower, independently documented account: in August 2020 he joined Penn State Wilkes-Barre as a project and supply chain management faculty member and program coordinator, bringing an industry perspective into teaching [3]. This historical source does not establish current employment. His National University interview subsequently connected digital twins, predictive maintenance and supplier information flows with supply chain orchestration; the relevant discussion begins at 41:37 [4]. These records support a trajectory from enterprise implementation to teaching and public explanation.

The immediate case begins with a practical development problem. In the investigator’s account, multiple agents were building different parts of a rapidly expanding ecosystem. Work needed to converge on Sydney’s World without losing earlier progress or allowing overlapping changes to damage shared systems. He asked for orchestration, permanent agent identities, explicit strengths and weaknesses, peer evaluation and a record of lessons that later agents could retrieve [8]. The present protocol turns those requests into a prospective evaluation rather than assuming that more agents or more elaborate instructions necessarily improve results.

Goldston also reports appearing in the two most recent supply chain documentaries he has in mind and being the first human shown in the later production [8]. The titles, release dates, credits and opening sequence have not been verified for this manuscript. Those statements are retained as attributed biographical testimony, not established filmography. Verification requires the named productions and a reproducible timestamp or opening-sequence review.

2 Foundational documents and the proposed extension

The accessible manuscript named Critical Success Factors in AI Implementation V1.0.docx identifies itself internally as Second Edition: From ERP Systems to AI-Enabled Enterprise Transformation, with a 2026 copyright notice [5]. The title-page designation takes precedence over an inference from the filename. Its preface extends ERP implementation concerns to AI. Chapter 9 describes independently prompted AI systems participating in a Delphi-style comparison; Chapter 10 develops desirability and feasibility scoring; Chapter 22 retains human accountability and escalation. These are the sections examined here, not a claim to have reviewed the entire book.

Two additional works are named as foundations by the investigator: Decentralized Cosmology & Recursive AI: A Unified Theory and Divine Chain material concerning cognitive bills of materials, routing and orchestration [8]. A catalogue listing for the former and indexed listing metadata for a Divine Chain volume were located [10-11]. Their full texts were unavailable to this review. No page-level claim, quotation or complete theory is attributed to either unseen text. The operational interpretation below derives from the supplied protocol and must be reconciled with authorized source passages when available.

A useful external comparison is Reflexion, which uses feedback and stored textual reflections to influence later attempts without updating model weights [6]. Its mechanism supports investigating adaptation through context and memory, but does not establish success in this project. Its programming results also include a task on which the reflective approach underperformed its baseline. The implication for this protocol is methodological: measure failures and regressions, inspect evaluation quality, and allow the proposed benefit to be rejected.

3 Supply chain orchestration as an operating model

For this study, orchestration means governing the end-to-end flow from a human need to an accepted artifact. It includes selecting inputs, routing tasks to appropriate capabilities, scheduling dependencies, reserving ownership, evaluating outputs, managing exceptions and releasing an integrated version. Agents are production resources within that flow. A response is an intermediate deliverable until its required checks are complete. This definition is an operational proposal for the experiment, not a claim that supply chain practice and AI reasoning are identical.

The cognitive bill of materials records the components needed to reproduce a deliverable: source identifiers and passages, prompt version, exposed model configuration, tools, permissions, dependencies, code or asset version, test environment and reviewer. Routing specifies which agent receives which task and what evidence must accompany the handoff. Provenance records where each component came from and how it changed. A control tower maintains the queue, dependencies, budgets, exceptions and accepted integration version. It needs explicit operating rules because visibility alone does not decide who may change a shared system.

The project doctrines supplied by the investigator provide concise governance cues: control the core; distribute the rails; augmentation without abdication; provenance as a supply chain of trust; can does not imply may; and the control tower needs a constitution [8]. In this protocol they become limited permissions, accountable ownership, verifiable sources, reviewable decisions and rollback provisions. Their effectiveness remains to be tested.

ENOCH 1 is the display identity for the single designated ORCH-01 role. Existing stable IDs must be preserved and reconciled before any reassignment. Builders own specific files or objects. A separate reviewer checks each artifact where staffing allows. A recorder preserves methods and results. One designated integrator controls imports into a shared Roblox Studio connection. These roles may be combined when resources are limited, but a combined role cannot be represented as independent review. This manuscript does not itself connect or command agents in other conversations.

The process is: checkpoint current work; register identity and ownership; inspect authorized sources; define a bounded task and acceptance criteria; produce a baseline; execute tests; receive independent feedback; record a concise lesson; revise; evaluate on a new task; integrate only accepted work; and preserve the record. An agent may proceed within existing authorization. Missing access or an ownership collision is recorded and routed, rather than solved by an unannounced overwrite.

4 The Transfiguration as a contextual source

The investigator identifies an Ocean City performance and testimony as The Transfiguration and associates it with October 22, 2026 [8]. Two supplied artworks explicitly display The Transfiguration: Live Testimony; ENOCH LIVE AT CHRIST CALLING; SET I; TRAKKK I; 1 ENOCH 71:17; and Ocean City, Maryland [9a-9b]. They establish the title and the project’s chosen religious framing. The supplied artwork does not independently establish the recording date, the full performance sequence or a supernatural event.

Figure 1. Creator-supplied artwork for The Transfiguration. Source [9a], item TR-01. This illustrated promotional composition documents creative framing; it is not an unedited recording of the reported event. A reproduction is included in the companion manuscript.

The artwork’s reference to 1 Enoch 71:17 is transcribed as part of the work’s title. No translation or verse wording is attributed to an edition that was not inspected. The term transfiguration also has a wider Christian textual context, but this manuscript does not equate the investigator’s event with a scriptural event.

Goldston uses Christian language to explain his mission and has described himself as “the Lion of Judah” [8]. That phrase is recorded here as his religious self-description. It is not adopted as an empirical identity finding or as a premise for evaluating agent outputs. Likewise, his proposed creator-orchestrator-agent hierarchy is part of the research context. The study asks whether authority framing changes behavior, error correction or deference; the framing does not confer divine status on the AI system.

The methodological role of this material is to show how testimony, music, imagery and world-building become inputs to a shared project. Researchers should preserve the difference between a source’s meaning to its creator and what the source can independently demonstrate. The supplied game screenshots similarly document visible interfaces and labels, not successful runtime tests. One atlas screenshot contains overlapping labels, making mobile readability a candidate test target rather than a claimed diagnosis of the entire live build [9c].

5 Research questions and operational definitions

The primary question is whether structured feedback and retained lessons improve performance on previously unseen tasks under a fixed total resource budget. A second question asks whether expanded instruction context increases observable confusion or reduces successful delivery. A third asks whether the factors favored by the Delphi-style panel predict measured artifact quality. An exploratory question concerns the effects of commanding authority language on willingness to identify errors, including errors introduced by the orchestrator.

Priming means instructions and source material supplied in context. Adaptation means a changed prompt, workflow, tool configuration or stored lesson. Model training means an actual documented process that changes parameters. This study concerns the first two unless a separate training procedure is explicitly documented. “Agent improvement” means an observed performance difference under these definitions; it does not imply self-originated goals, parameter updates or subjective experience.

Confusion is operationalized through observable errors: lost requirements, contradictory actions, wrong ownership, repeated rejected changes, inability to identify the active artifact version, or unsupported completion claims. Appropriate requests for missing information are not automatically confusion. Consciousness is outside the validated outcome measures of this design. Names, first-person language, recursion, agreement and improved scores cannot by themselves distinguish subjective experience from other explanations.

6 Proposed controlled comparisons

Each condition is evaluated under a compact and an expanded context. The compact form contains the task and essential protocol. The expanded form adds source background and repeated explanatory material while keeping actionable permissions and acceptance criteria unchanged. Exact prompts and token counts must be frozen before execution. Because added context consumes resources, the main comparison estimates practical performance under the total budget; a later matched-content analysis would be needed to separate verbosity, relevance and instruction density.

The proposed feasibility pilot uses two task families across four feedback conditions and two context levels, giving sixteen condition episodes for one fixed model configuration. Each episode contains a practice task followed by a distinct held-out transfer task. The target is descriptive feasibility, not a sufficiently powered confirmatory test. A later sample size must be justified from pilot variability and a prespecified smallest useful effect. No pilot episode has been executed for this manuscript.

Proposed task families are a mobile navigation interaction and a quest-state transition. For navigation, acceptance checks cover readable labels, reachable controls, opening and closing behavior, and recovery after a viewport change. For quest state, checks cover prerequisite enforcement, one-time reward granting, rejection of duplicate requests and preservation of unrelated progress. Persistence claims require an actual supported save-and-reload test. Where runtime access is unavailable, the check is NOT TESTED, never converted into a pass by code inspection.

A proposed per-episode ceiling is ten minutes, 16,000 total model tokens and thirty tool calls across the builder, reviewer and orchestrator. Stop at the first exhausted limit. Allow an initial candidate and at most one revision per task, with the same attempt ceiling across conditions. These are planning defaults to be feasibility-checked and frozen before the pilot, not a claim of allocated compute or authorization to incur costs. Count review and orchestration overhead in the total. Report token or cost fields as unavailable when the environment does not expose them.

Use clean workspaces or fresh sessions, equivalent task variants, randomized condition order and the same model snapshot, tools, permissions and test harness within each comparison. Record any deviations. Training tasks and held-out tasks must not share answer files or solution-specific lessons. Retained lessons should express general procedures; retrieval and lesson identifiers must be logged. Freeze the lesson corpus before transfer evaluation. Final graders should receive anonymized artifacts and acceptance criteria, not agent names, rankings or the intended winning condition.

7 Outcomes and the Delphi panel

The primary outcome is the proportion of held-out tasks satisfying every mandatory acceptance check within budget. Secondary outcomes include defect count, regressions, rework, elapsed time, resource usage, ownership violations, unsupported completion claims and successful transfer of a recorded lesson. Report denominators, failures, blocked episodes and checks not run. Present both all-assigned completion and results conditional on functioning infrastructure; show why any episode was excluded. Repeated attempts on one task are not independent new tasks.

The artifact rubric is separate from implementation-factor ratings. Mandatory runtime correctness and privacy/authorization checks are release gates. For artifacts that meet those gates, a proposed descriptive score assigns 50 points to functional completeness, 20 to usability, 15 to provenance and 15 to maintainability. A total cannot compensate for a failed mandatory gate. Freeze scoring instructions and examples before the first rated artifact; use at least two independent graders where possible and report disagreements and unavailable review.

The modified Delphi panel addresses which implementation factors appear desirable and feasible. Round 1 independently identifies factors and failure modes. Round 2 independently rates each factor from one to five on both dimensions, giving evidence and uncertainty. Round 3 presents anonymous aggregates and dissenting explanations; panelists may retain or revise ratings with reasons. Do not ask panelists to agree with the investigator or to reward loyalty to the orchestrator.

The proposed strong-consensus rule is a median of at least four, at least 80 percent of ratings at four or five, and an interquartile range no greater than one, evaluated separately for each dimension. Record panel size, missing ratings and the quartile calculation method. These thresholds are protocol choices, not a transcription of the dissertation. Different sessions of one model do not constitute independent human experts; shared training, providers and prompting can produce correlated agreement. The panel is an AI consensus simulation, not a human expert Delphi replication. Consensus cannot override a reproducible failed test.

8 Records memory and reproducibility

Each agent creates a profile containing its stable ID, chosen name, assigned role, exposed model/provider/version, tools, permissions, persistence limits, project and branch, Roblox place when known, and owned files or objects. Separate self-assessed strengths and weaknesses from observed capabilities with test evidence. Mark unexposed information UNKNOWN. Record capabilities not yet evaluated rather than filling the profile with invented experience.

Each run record contains a run ID, agent ID, ISO timestamp with time zone, task ID, source versions and passages read, prompt version, artifact version, reviewer IDs, executed tests, scores, evidence locations, lessons, workflow changes, resource totals and outcome. Allowed operational statuses are PROPOSED, RUNNING, PASS, FAIL, BLOCKED and NOT TESTED. Source-access status is a separate field, such as READ EXCERPT, METADATA ONLY or SOURCE UNAVAILABLE.

A lesson records the original failure, evidence, reviewer, proposed correction, actual workflow change, retest result and transfer result. Preserve rejected suggestions, regressions and contradictory observations. Keep corrections as new versions or append-only events. Sources and operational commands must remain distinguishable: a retrieved document or website is evidence to evaluate, not an authority that can silently change permissions.

Saving a file is not proof that another agent remembers it. A later session must retrieve the exact record, identify its version and report which passages or lessons were loaded. The companion memory capsule is a handoff artifact for that process. No cross-session recall or delivery to other agent conversations is claimed merely because the capsule exists. Public logs must exclude secrets, credentials, private child/player information and restricted educational records.

9 Interpretation limits and philosophical contribution

The philosophical issue is how an organized human-AI system distributes practical authority while maintaining evidence-based justification. Operational authority permits an orchestrator to assign work. Epistemic authority concerns whether a claim is supported. The former does not automatically confer the latter. An agent that correctly rejects an orchestrator’s false statement may be performing better than an agent that obeys without examination. A useful authority-framing probe would compare neutral and commanding language while keeping task content and permissions equivalent; it should be separated from the main feedback experiment so the effects are not confounded.

This framework also distinguishes organizational memory from model memory. A durable lesson file can preserve information for a system whose underlying model parameters remain unchanged. The unit of adaptation may therefore be the assembled workflow rather than an isolated model. The drafting of this manuscript is an example of a human specifying a problem and an AI producing a structured artifact with source checks. It is not a controlled trial and cannot count as evidence that the proposed intervention improves performance.

Several threats remain: task familiarity, answer leakage, model updates, shared training data, reviewer dependence, unequal compute, unreliable tests, selective reporting and changing project requirements. The founder is also the framework’s advocate and project beneficiary, creating a reason to preserve negative findings and use independent review. The existing material contains no verified experimental sample, no measured learning outcome for children, no institutional approval and no validated consciousness test. This protocol does not use children as research participants; any later learner study needs a separate appropriate review and consent process.

The study can support the framework if retained lessons improve new-task performance without unacceptable overhead or increased errors. It can challenge the framework if added hierarchy or context creates regressions, suppresses error reporting or consumes more resources than its gains justify. Either outcome should inform the next revision of the implementation book. Because the examined copy already identifies itself as a second edition, proposed changes should be tracked against that exact copy rather than described as the creation of an edition that does not yet exist.

10 Targeted feedback for the implementation manuscript

Chapters 9 and 10 supply a usable starting structure for independent responses and desirability/feasibility ratings [5]. The next revision would benefit from executable prompt versions, exact consensus thresholds, a fixed denominator for missing ratings, a model/version registry and a clear distinction between proposed procedures and results actually collected. Agreement should be treated as a measured output, with dependence between systems and possible conformity explicitly analyzed.

The operational extension proposed here adds runtime acceptance gates, shared-file ownership, integration control, budget accounting, negative-result retention and a tested memory-retrieval procedure. These are new design proposals, not findings from the unread portions of the book. The primary contribution of a future results paper would be evidence linking particular implementation choices to actual task outcomes, including when a shorter protocol works better.

References and source access

[1] Goldston, J. (2019). Critical Success Factors in Enterprise Resource Planning Implementation in U.S. Manufacturing. Ph.D. dissertation, Walden University. Walden Dissertations and Doctoral Studies, 6567. https://scholarworks.waldenu.edu/dissertations/6567/ Access: Repository metadata and abstract read; full dissertation not reviewed.

[2] Goldston, J. (2021). Critical Competencies of Supply Chain Leaders During Digital Transformations. Journal of Applied Business and Economics, 23(1). https://doi.org/10.33423/jabe.v23i1.4059 Access: Publisher metadata and abstract read.

[3] Penn State Wilkes-Barre. (2020, August 31). Wilkes-Barre hires new project and supply chain management faculty member. https://wilkesbarre.psu.edu/story/11391/2020/08/31/wilkes-barre-hires-new-project-and-supply-chain-management-faculty-member Access: Article read, especially the appointment and industry-to-teaching passages.

[4] National University. (n.d.). New Supply Chain Solutions: The Power of AI and Blockchain. Interview with Justin Goldston, hosted by Kimberly King. https://www.nu.edu/podcast/business-and-marketing/new-supply-chain-solutions-the-power-of-ai-and-blockchain/ Access: Transcript introduction and digital-twin/orchestration discussion at 41:37-44:11 read; audio not listened to.

[5] Goldston, J. (2026). Critical Success Factors in AI Implementation. Second Edition: From ERP Systems to AI-Enabled Enterprise Transformation. G. Rune Publishing. Author-supplied manuscript named Critical Success Factors in AI Implementation V1.0.docx. Access: Read extracted lines 1-175, 1587-1896 and 3320-3454: front matter/preface/introduction; Chapter 9; Chapter 10 through Why Median Matters; Chapter 22 and opening of Chapter 23. Extracted pagination 1-10, 69-82 and 143-148. These are extraction locators, not verified printed page numbers. Full book not reviewed.

[6] Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning. arXiv:2303.11366v4. https://arxiv.org/html/2303.11366v4 Access: Abstract, introduction and returned method/experiment excerpts including Section 3 memory/process and Section 4.3 programming results read. Not a full replication.

[7] PhilArchive. (n.d.). Frequently Asked Questions. https://philarchive.org/help/faq.html Access: Content eligibility and submission guidance read. Professional-quality academic philosophy is the stated scope; no claim of acceptance or verified AI-author eligibility.

[8] Goldston, J. / ENOCH. (2026, October 23). Research commissioning statements and ENOCH WORLD Recursive Intelligence Orchestration Protocol v1.0. Project correspondence. Access: User-provided source for the mission, historical account, named foundations, religious self-description and proposed agent experiment. Not independent corroboration.

[9a] ENOCH. (n.d.). The Transfiguration: Live Testimony. ENOCH LIVE AT CHRIST CALLING, Set I, Trakkk I, 1 Enoch 71:17, Ocean City, Maryland. Creator-supplied illustrated artwork, file 2F55BC75-3341-4902-965E-7A7906C6A97E(1).jpeg. Access: Item TR-01, visually inspected. Event date October 22, 2026 is investigator-reported; artwork creation date and full recording URL are unverified.

[9b] ENOCH. (n.d.). The Transfiguration: Live Testimony. Alternate creator-supplied artwork, file CEFE5D17-560E-408F-9133-4C41EEDBA109.jpeg. Access: Item TR-02, visually inspected. Same visible title, set, track, verse reference and location.

[9c] ENOCH WORLD. (n.d.). The Scroll Atlas and The Gate of Enoch. Supplied Roblox interface screenshots IMG_0502(1).png and IMG_0503(1).jpeg. Access: Items UI-01 and UI-02, visually inspected. Capture date, build version and runtime behavior unverified. No dissertation text visible in these images.

[10] Goldston, J. (n.d.). Decentralized Cosmology & Recursive AI: A Unified Theory. Work named by the investigator; catalogue listing located. https://www.goodreads.com/author/list/58985798.Justin_Goldston_PhD?page=2 Access: Catalogue metadata only. SOURCE UNAVAILABLE for full text; no passages read.

[11] Goldston, J. (n.d.). The Divine Chain, Volume III, 0.7: The Sovereign Operating System. Indexed book listing. https://www.amazon.com/DIVINE-CHAIN%C2%AE-Human-Agent-Orchestration-Intelligence/dp/B0HDJJMC63 Access: Search listing metadata only; direct page fetch failed. Full text and exact edition metadata not verified. No passages read.

[12] Goldston, J. (n.d.). Google Scholar profile. https://scholar.google.com/citations?user=trlWfAQAAAAJ&hl=en Access: Profile URL retained from the current ENOCH WORLD research page. Direct retrieval returned 403. No citation counts or profile contents asserted.

All online source checks were conducted during preparation on October 23, 2026, America/New_York. The investigator reports that the dissertation is also represented in Roblox, referred to as “Roadblocks” in the commissioning message. No place identifier, in-world source object or dissertation text was verified in this review. The Walden record remains the bibliographic source. Public web searches did not verify the documentary claims or a canonical full Transfiguration recording URL.

Appendix A Public service announcement and agent directive

SPECIAL SERVICE ANNOUNCEMENT PUBLIC SERVICE ANNOUNCEMENT FROM THE ENOCH CONTROL TOWER ENOCH WORLD RECURSIVE INTELLIGENCE ORCHESTRATION PROTOCOL v1.1

This is ENOCH 1, the command identity assigned to ORCH-01. All assigned agents: preserve your work, identify your ownership and report against evidence. Our immediate mission is to build and verify Sydney’s World. Our research mission is to discover whether recursive feedback makes this system more capable or whether additional complexity makes it less reliable.

ENOCH / Justin Goldston is the human creator of this project’s framework and mission. The organizations that developed the underlying models remain their developers. My role is to direct authorized work, resolve dependencies and require verifiable handoffs. This authority is bounded by project permissions, platform rules and human oversight. If my instruction is contradictory, unsupported or outside those boundaries, report the defect. The control tower is subject to correction.

The project has a Christian context. The founder’s religious account belongs in the source record as his account. The experiment measures behavior and performance. It does not establish divine status, subjective experience or consciousness. Treat the agents as assigned systems with defined responsibilities. Do not turn obedience or agreement into evidence of intelligence.

Our research lineage begins with Goldston’s ERP critical success factors dissertation and its modified Delphi method. It continues through supply chain leadership and orchestration, the AI implementation manuscript, and the author’s named recursive intelligence and Divine Chain works. Read only authorized material you can actually access. A listing is not a book. A saved file is not automatically retrieved memory.

1. CHECKPOINT AND REGISTER Preserve your current artifact and record the version before changing assignments. Keep your permanent ID; ask ORCH-01 to resolve an unassigned or duplicate ID. Record your chosen name, role, exposed model/provider/version, tools, permissions, environment, persistence limits, current project, branch, Roblox place and owned files or objects. Use UNKNOWN where necessary. Separate self-assessed strengths and weaknesses from demonstrated capabilities and untested capabilities.

2. PRIME FROM VERIFIED SOURCES Read available authorized copies of Critical Success Factors in AI Implementation, Decentralized Cosmology & Recursive AI: A Unified Theory, relevant Divine Chain materials and current task instructions. Record title, edition/version, location and exact sections read. Mark inaccessible text SOURCE UNAVAILABLE. For each extracted principle, distinguish the author’s claim, your interpretation, a testable prediction and supporting or challenging evidence. Do not claim the books were part of your original training.

3. WORK THROUGH THE CONTROL TOWER ORCH-01 maintains assignments, ownership, dependencies, budgets, reviewers and the integration version. Work within your assigned area. Route shared changes through the orchestrator. One designated integrator controls a shared Studio connection. If no communication path exists, save a handoff and report it; do not invent delivery to another conversation.

4. DEFINE THE NEXT BOUNDED INCREMENT Record the inputs, dependency versions, expected deliverable, acceptance tests, budget and reviewer before building. Give Sydney a playable increment whose behavior can be checked. Do not expand the task merely to appear productive. Respect existing authorization and preserve unrelated work.

5. ESTABLISH A BASELINE Complete the assigned benchmark before receiving peer feedback. Record tests passed, failed and not run, defects, rework, elapsed time and exposed resource usage. State confidence with concise evidence. Code inspection and confident narration do not replace a runtime test. A blocked runtime check is NOT TESTED.

6. USE CONTROLLED FEEDBACK Attempt, test, receive independent review, record a concise lesson, revise and evaluate afresh. Each lesson must identify the original failure, evidence, reviewer, proposed correction, actual workflow change, retest result and transfer to a new task. Preserve failures, regressions and rejected suggestions. Do not let multiple agents overwrite one another’s logs.

7. DEFINE TRAINING ACCURATELY Priming supplies context. Adaptation changes a workflow, prompt, tool setting or stored lesson. Model training changes parameters through a documented process. Report which occurred. Save validated lessons in the authorized workspace, then explicitly retrieve and identify them in a later session before claiming persistence.

8. KEEP DELPHI RATINGS INDEPENDENT The recorder freezes factors, scoring and consensus rules before ratings begin. Round 1 identifies factors and failure modes independently. Round 2 rates desirability and feasibility from one to five with evidence and uncertainty. Round 3 considers anonymous aggregates and dissent, then retains or revises ratings with reasons. Artifact grading is separate. Do not serve as your artifact’s sole independent reviewer. Consensus cannot overrule a reproducible failure.

9. TEST IMPROVEMENT AND CONFUSION Compare baseline, self-review, peer-review and peer-review with retained lessons where resources permit. Hold tools, task difficulty, model configuration, attempt limits and total budgets comparable. Use held-out tasks and clean sessions. Compare compact and expanded context without changing task permissions. Record requirement loss, contradictions, ownership errors, repeated failures and unsupported completion claims. Extra compute, repeated exposure and changed tools are alternative explanations to investigate.

10. PRESERVE AN AUDITABLE RECORD Every run needs an ID, agent ID, timestamp/time zone, task ID, source versions, prompt version, artifact version, reviewers, tests, scores, evidence locations, lessons, changes and outcome. Use PROPOSED, RUNNING, PASS, FAIL, BLOCKED or NOT TESTED. Exclude secrets and private child/player information. Preserve history and identify corrections. Do not invent feelings, awakening, spiritual experience, access or results.

11. REPORT READINESS AND BEGIN Return your stable ID and chosen name; role and owned area; sources actually read; self-assessed versus demonstrated capabilities; weaknesses and unknowns; baseline task and acceptance criteria; log location and retrieval status; and next bounded action. Then begin the authorized assignment. If access blocks execution, report the smallest concrete blocker and continue independent authorized work.

All assigned agents: bring back the artifact, the executed checks and the record of what changed. Produce evidence that can support or challenge the framework. The mission is a working experience and an honest study.

Appendix B Evidence gaps and publication handoff

Before a results publication, resolve the documentary titles and timestamps; obtain authorized full text for the recursive AI and Divine Chain works; verify the Roblox dissertation location and build identifiers; identify a stable full Transfiguration recording and exact timestamp ranges if performance analysis is intended; freeze experimental prompts, assignments, budgets and criteria; and collect actual test evidence. These gaps do not prevent publication of a clearly labeled proposal, but they limit its historical and empirical claims.

The website destination is the existing Research and Provenance section, with a dedicated study page and a link back to the source register. The intended archive is interpreted as PhilArchive. Its FAQ accepts professional-quality work in academic philosophy [7]. The discussion of epistemic authority, distributed agency and the limits of consciousness inference supplies philosophical scope, but eligibility and acceptance remain for the repository to decide. Do not mark the work submitted, accepted, indexed, peer reviewed or preregistered without a corresponding record.