Cómo Se Construyó Este Corpus

A Public Manifesto for Open, Agentic Theological Research

By Elijah Faviel

Portfolio version: V1

Publication status: public V1 preview before the first stable release

People are right to ask how one author could publish a connected body of books, research records, study tools, and multilingual editions in such a concentrated period.

The short answer is that the work did not begin when the books appeared. It began years earlier with Rethinking Reality. It grew through a large and expensive AI research system. And despite the speed of the models, it moved through a slow human process of reading, redirection, judgment, and cross-verification.

AI made the scale possible. It did not make the conclusions true. This is the longer and more honest account of what happened.

Before the Corpus, There Was One Book

I began planning Rethinking Reality in 2019. The book took years to develop through research, theological and scientific questions, pastoral conversations, repeated restructuring, and the ordinary difficulty of trying to say something true about a complicated world.

It was the first book in this corpus, and it received pastoral and theological feedback during its development. But its notes kept producing questions that one accessible book could not responsibly carry. How should biblical, historical, empirical, philosophical, and pastoral claims relate without borrowing authority from one another? How could original-language Scripture remain the norm for Christian confession while empirical claims remained answerable to evidence? How should early Christian witnesses be used without turning the fathers into a second canon? How could a framework mark the difference between direct reading, synthesis, analogy, judgment, and genuine uncertainty?

Those questions became the seedbed of Divine Design Framework, or DDF.

The use of AI did not begin with DDF. I was already building AI research pipelines while developing Rethinking Reality, and I estimate that roughly half of the research that eventually fed the wider corpus began there. DDF inherited that material and experience, then made the method far more formal, wide, deep, and demanding about cross-verification.

Why DDF Had to Be Built

In conversations with pastors, engineers, thinkers, and people trying to make sense of Scripture and modern life, I kept finding the same need. They needed more than another set of answers. They needed a disciplined way to research biblical concepts, test connections, preserve distinctions, and turn what survived into books and practical tools that could still be corrected.

The scope was larger than any one specialty. A person can devote a lifetime to biblical studies, patristics, philosophy, physics, biology, psychology, clinical care, history, artificial intelligence, or pastoral practice and still remain finite. Serious work across all of them ordinarily requires a multidisciplinary community and generations of accumulated expertise.

This project does not pretend that earlier scholarship failed or that the research came from nowhere. It depends on Scripture, ancient witnesses, primary documents, generations of scholarship, modern research, and the Church's long memory. To research “from scratch” meant something narrower: I did not want to adopt one modern theological system, denominational handbook, philosophical school, or popular summary and make every source fit it. I wanted to return as close as possible to original-language Scripture, ancient textual witnesses, patristic documents, primary historical materials, technical papers, and observed reality, then rebuild the connections under pressure.

AI made that scale attainable.

Why AI Did Not Make the Work Fast

My own background made this method conceivable. I am a principal engineer and an author of fiction and nonfiction. My career has included building large-scale production systems and working in AI across multiple companies. I am comfortable with agents, pipelines, versioning, failure modes, and very long documents.

That background explains why I could build and inhabit this process. It does not make me a theologian, historian, scientist, clinician, or biblical-languages specialist by title. It does not validate a single conclusion. Credentials are context, not warrant.

Over many months, I used a changing environment of models and many specialized agents. The work drew from three model families: xAI's Grok, OpenAI's GPT, and Google's frontier models. I always used the frontier release from each family and the highest reasoning level available to me at the time. When a new generation arrived in any family, I reran important questions and recertified the relevant conclusions.

This mattered because each family showed recurring emphases and appeared drawn toward certain kinds of ideas. Moving work between them helped expose when a conclusion might be an echo of one model lineage, a higher-order loop of model assumptions, rather than something the sources warranted. Cross-family comparison did not create independent peer review, but it added another check against correlated reasoning.

The larger V1 effort represents thousands of hours of agentic work and tens of thousands of dollars in compute and research costs. Repeating difficult research across model families and generations at their highest reasoning levels contributed substantially to that cost. Models mapped unfamiliar fields, divided large questions into researchable parts, inspected source families, and compared interpretations. They also translated and reconstructed documents, searched for counterevidence, built rival accounts, tested bridges between disciplines, found contradictions, drafted prose, and returned to the same claims under new constraints.

That sounds fast. It was not.

The models could place biblical exegesis, patristic theology, historical reconstruction, philosophy, physics, biology, psychology, clinical research, institutional analysis, and AI engineering in front of me within one project. Those fields do not share one vocabulary, one standard of evidence, or one form of argument. Before I could direct the next pass, I had to understand what a research document was saying, why its sources mattered, what the strongest competing argument was, and how much weight its conclusion could carry.

I read the research documents presented for retention and integration. I read the patristic and theological material necessary to understand the historical arguments. I worked through the competing positions instead of treating an AI summary as self-validating. I had to learn enough of each local field to notice when an agent was repeating itself, hiding a disputed premise, misclassifying a source, or joining two domains too quickly.

Every research document in the durable record, and every research result that materially shaped the manuscript, passed through that human process. The work did not move directly from a model response into DDF or from DDF into another book.

In engineering terms, human comprehension became the rate-limiting step. AI could generate another research pass in minutes. Reading it, comparing it with the underlying materials, understanding its rivals, and deciding what research had to happen next could take days. Cross-verifying a biblical claim against original-language work, early reception, doctrinal history, empirical evidence, and possible human consequences took longer still.

The concentrated publication period is therefore not a story of instantaneous authorship. AI made breadth and iteration possible. Human reading made responsible synthesis slow.

What the Research Process Actually Looked Like

There was no clean conveyor belt in which one agent researched, another verified, and a final model wrote the book. The work behaved more like an ecology: many loops operating at different scales, sometimes in parallel, sometimes in conflict, and often reopening questions that had seemed settled.

The exact orchestration changed with the problem, but six kinds of work recurred.

1. Map the whole field. Broad agents separated a question into its textual, doctrinal, historical, scientific, philosophical, clinical, technical, and practical surfaces. Other agents explored adjacent fields, rival schools, neglected periods, edge cases, and questions the original frame could not yet see.
2. Go narrow and return to sources. Deep-research passes focused on one word, text, claim, doctrine, experiment, historical episode, clinical risk, or disputed bridge. When summaries were not enough, the work returned to Hebrew, Aramaic, and Greek Scripture; ancient versions and witnesses; patristic and primary historical documents; technical papers; datasets; standards; and versioned model reports. Some materials were transcribed, translated, or compared directly across languages. Many questions needed a second, third, or later deep dive.
3. Build the strongest opposition. Agents searched for counterexamples, contrary evidence, category errors, hidden assumptions, unsafe applications, and stronger rival explanations. The purpose was not performative skepticism. It was to stop the preferred account from grading its own exam.
4. Attempt synthesis, then test the bridge. Separate passes tried to connect local findings without allowing one field to govern another improperly. Every proposed bridge had to return to exact definitions, negative cases, the proper source for each claim, and the best alternatives. Many elegant connections weakened into analogies. Some disappeared.
5. Use human direction where the system stalled. Models often entered a conceptual attractor: they repeated one family of ideas in new language, defended an inherited frame, or optimized for a balanced and persuasive answer when the evidence required a colder result. More autonomous iteration then made the circle deeper, not wider. At those points I changed the objective or the search space. I required truth before fit, separated direct biblical claims from canonical synthesis, later doctrine, empirical findings, analogy, and authorial judgment, and rejected warmth or doctrinal familiarity as substitutes for warrant. I redirected stuck inquiries into areas such as mathematical discovery, scientific model formation, sleep paralysis and anomalous experience, clinical differentials, institutional abuse, and negative cases. Sometimes I demanded a better rival. Sometimes I narrowed an overloaded term. Sometimes I reset the work from primary sources.
6. Recertify at several scales. Mature claims were revisited at sentence, section, chapter, whole-book, and portfolio levels. Different passes checked biblical exactness, source roles, claim strength, contradictions, current evidence, embodied risk, bibliography, dependencies, and revision conditions. Downstream books were checked again so that simplification for a church, household, catechetical, or public audience did not quietly make a claim stronger.

These were loops, not sealed phases. A pastoral danger could reveal a false doctrinal compression. A rival account could force a new translation. A source check could dissolve an attractive analogy. A production audit could send a finished-looking chapter back into research.

Multi-agent scale mattered because one model in one continuing context tends to inherit its own framing. Separate agents and fresh contexts allowed breadth to be distinguished from depth, proposal from opposition, extraction from synthesis, and drafting from recertification. But these agents were not independent peers. Models trained on overlapping data can share the same error, defer to one another, or reinforce the first framing they receive. Five agents agreeing is not the same as five scholars independently examining a source.

Human direction was not an independent warrant either. I could anchor the models, import a bias, or ask a question that favored the conclusion I wanted. That is why the record preserves major methodological corrections and why each claim still has to survive the relevant sources, evidence, rivals, risks, and revision conditions. My direction determined where to look. It did not determine what was true.

What AI Could and Could Not Contribute

Large language models are trained statistical sequence models, not witnesses with access to truth. Attention-based architecture, large-scale pretraining, instruction tuning, human feedback, retrieval, search, code execution, and file inspection give them remarkable abilities. They can reorganize a difficult question, compare many texts, expose a missing distinction, generate candidate explanations, and draft useful prose.

They can also make a wrong answer arrive faster, cleaner, and with footnotes.

The research system was designed around several recurring failures:

• Fluent invention and stale knowledge. Plausible quotations, sources, facts, and current claims were checked against the underlying document, provenance, date, model version, or empirical record.
• Sycophancy and premature harmony. Agents were asked to contradict the preferred conclusion, construct serious rivals, name negative cases, and allow “underdetermined,” “analogy only,” “in tension,” or “contradicted” as valid results.
• Correlated agreement. Different models, roles, prompts, source routes, and fresh contexts were used to diversify the search. Agreement among models was never counted as independent evidence. Work on correlated errors across large model populations, including Kim et al., reinforces why apparent multi-model consensus cannot carry that weight.
• Context loss, circularity, and false self-correction. Work was divided into bounded claim families with stable identifiers, ledgers, source maps, and later whole-book audits. A stalled loop was given new evidence or stopped for human redirection instead of being trusted to think itself free.
• Automation bias. Generated reasoning was not treated as a transparent record of model cognition, polish was not treated as proof, and final authorship and responsibility remained human.

These controls reduce risk; they do not eliminate it. They are also consistent with the broader AI literature:

• The basic model architecture and the limits of next-token prediction are described by Vaswani et al. and Kalai et al..
• Research on human-feedback training and sycophancy includes Ouyang et al. and Sharma et al..
• Confabulation, long-context failure, unreliable self-correction, and unfaithful rationales are documented by the NIST Generative AI Profile, Farquhar et al., Liu et al., Huang et al., and Turpin et al..
• Multi-agent research reports both gains and persistent failure modes: Du et al., Liang et al., Oh et al., Choi et al., and Becker et al..

AI was therefore a research instrument and generative partner. It was not a source of revelation, a theological authority, an independent witness, or a replacement for the people and realities being studied. Model output created work to be tested. It did not end the test.

What DDF Became

DDF is an argument, a field guide, and an audit trail. It states its Christian proposal openly while leaving empirical, historical, philosophical, clinical, and technical claims answerable to the evidence and methods of the fields competent to judge them.

Its major claims are governed through claim cards and shared profiles. These record the assertion, domain and scale, claim type, governing source, warrant, confidence, strongest rival, limits, risks, dependencies, and the conditions that would require revision. The V1 register contains eighty-nine stable claim IDs. The method also permits a bridge to fail. Putting two true sentences next to each other does not establish a relationship between them.

The public research library shows how this discipline developed. It preserves claim extraction, source checks, contradiction maps, chapter research, high-risk findings, calibration, bibliography matching, gap analysis, and repeated whole-book stress tests. It also preserves important corrections and the current durable method. Earlier development states remain recoverable through version history rather than being presented as current guidance.

At the V1 snapshot, the English DDF manuscript and durable research library contain 59,317 source lines. The ledger moves through thousands of numbered conclusions and repeated textual, historical, scientific, clinical, source, rival, safety, and currentness audits.

That volume is evidence of process, not evidence of truth. The public record is also not a replay of every temporary prompt or discarded branch. It preserves the durable state needed for examination and revision: source trails, claims, audits, decisions, manuscript history, and exact repository snapshots.

What the Framework Concluded

The research repeatedly found that early Christian confession was more embodied, relational, sacramental, intellectually serious, and resistant to both material reduction and spiritual escape than many modern summaries suggest. It also found that Christian theology does not need science to pretend to prove doctrine, or theology to control empirical results in advance.

DDF's central proposal is that reality is one created order, given through the personal Logos and received by creatures through distinct but integrated forms of embodied, rational, relational, moral, and spiritual life. The framework preserves distinctions because truth requires them, then asks how those realities belong together.

That proposal aligns in important ways with Scripture's canonical movement and central early Christian emphases. Creation is good. The Word truly became flesh. Salvation is not escape from embodiment. Truth and love cannot be severed. Resurrection and new creation are the Christian horizon.

Alignment does not mean that every father agreed, every historical problem is settled, or DDF is identical to the early Church. The records preserve disagreement, development, uncertainty, and places where later doctrine is a disciplined synthesis rather than direct biblical wording.

From One Framework to a Library

The chronology matters. DDF did not generate Rethinking Reality from nothing. Rethinking Reality came first; its questions and research helped generate DDF. Once DDF became explicit, it returned to the first book as an audit and alignment architecture.

The later books were developed through the DDF core, but they are not DDF chapters wearing new covers:

• Cognitive Resonance Model presents a correctable model of how people receive, resist, and integrate claims.
• Truthful Communion applies the architecture to church life, authority, care, protection, repentance, and repair.
• The Faith That Holds carries it through an ordinary-language catechism.
• Households of Formation applies it to children, caregivers, households, church support, safety, and responsibility.
• DDF Church Blueprint turns it toward planting, governance, worship, operations, accountability, crisis, succession, and closure.
• DDF in Action demonstrates the framework through shorter public essays.

The three-language boundary began with Rethinking Reality. I personally revised its English and Spanish editions because I am a native speaker of both languages. I did not perform its Indonesian-language revision. Gabriela Tifany, a native Indonesian speaker who has helped me edit books in the past, revised that Indonesian edition and contributed Indonesian linguistic and cultural context. The expanded portfolio is available in English, Spanish, and Indonesian because these are the three languages for which I have direct access to native speakers. The newer companion localizations remain V1 preview work and still require direct native-speaker review before stable release.

If you are a native speaker of another language and can honestly commit the hundreds of hours required to verify and localize this work in your language, please contact me. I would be happy to work with you.

Agents generated, expanded, criticized, and revised drafts, while the source rules, DDF coverage gate, ledgers, safety boundaries, and cross-book audits controlled what could move downstream. The corpus took its concentrated shape over roughly one to two years. It stands on the longer development of Rethinking Reality, the earlier AI pipelines, the human reading of their research, and the accumulated sources on which the entire project depends.

Publication Opens the Circle

Except for the earlier pastoral and theological feedback on Rethinking Reality, V1 was not co-developed through a formal external reviewer program. Internal agents are not independent peers. Automated audits are not pastors, textual scholars, historians, scientists, clinicians, safeguarding professionals, engineers, translators, or readers living with the consequences.

The scale is unusual. It is not a substitute for reception by the Church or examination by qualified people.

The website publishes the DDF documents, research method, source trails, and available portfolio materials for download. Readers can inspect the documents directly, place them into the AI system of their choice, rerun questions, compare claims with primary sources, find contradictions, or build stronger counterarguments. The research hub provides the downloads, version information, and public feedback path.

That invitation includes biblical scholars, original-language readers, and patristic, historical, and systematic theologians. It includes pastors and church members; scientists and clinicians; safeguarding, legal, privacy, financial, and governance professionals; engineers and AI researchers; Spanish and Indonesian reviewers; parents, skeptics, and readers formed in traditions different from mine.

Feedback is recorded against an exact V1 snapshot. Accepted, modified, deferred, and declined findings receive a rationale, and accepted changes remain traceable to their implementation. Participation is not represented as endorsement. A criticism is not rejected because it came from outside the framework.

The portfolio is marked V1 and remains V1 throughout this first public examination. It does not advance to a stable version until it passes the published stable-release gate.

Read It Against Reality

Do not trust this work because it used AI. Do not dismiss it because it used AI. Test it.

Download DDF and ask:

1. Which claims come directly from primary sources, and which are later syntheses?
2. Where does the language outrun the evidence?
3. What is the strongest rival account or contrary case?
4. Does a cross-domain metaphor preserve its meaning, or hide a category error?
5. Could this conclusion become false, unsafe, or distorted when another book applies it?

Then send what you find through the public feedback path.

The aim is not to make AI sound theological or theology sound scientific. It is to pursue one reality with enough discipline for different forms of truth to meet without being confused. It also requires enough humility for every part of the system to be corrected when reality presses back.

The process is large because the questions are large. The records are public because scale without transparency should not be trusted. Publication begins examination; it does not end it.

Read it. Test it. Break what is false. Strengthen what is true. Help build what the Church and the world can actually use.