The question has an owner
I set the problem, context, standards, and consequence before an agent begins. The system does not invent its own mandate.
Inside the FrankX workspace
This worked example uses one published essay to show the source set, the specialist review, the disagreement that mattered, and the decision I made.
The linked synthesis, date, source registry, evidence grade, and limitations are public. This pass map explains the review method; it is not presented as a time-stamped execution log.
Worked example · Published July 16, 2026
Source set
DeepMind’s delegation framework, WORKBank, Ulloa et al., a QJE field study, and NIST’s active work on agent identity and authorization.
Evidence Synthesis
Mapped what delegation, workplace AI, mixed-initiative systems, and governance research support—and where the evidence is only adjacent.
Contradiction Review
Rejected the tempting claim that AI delegation gives introverts a proven advantage; current studies do not establish it.
Editorial Architecture
Turned the synthesis into a seven-field intent contract, five decision-right levels, limitations, and open questions.
Frank’s decision
Present Intent Architecture as a FrankX synthesis—not a settled academic field. Keep the evidence grade at C, name the limits, and make every consequential action cross a human boundary.
Source to publication
The sequence is deliberately legible. A visitor should be able to see where the source ended, where an agent inferred, and where I made the decision.
A question, book, conversation, dataset, workshop, or unfinished system.
Agents gather sources, compare interpretations, expose gaps, and build a testable draft.
Frank challenges the synthesis, cuts what is weak, and chooses what should become public.
Research, architecture, a guide, book intelligence, a prototype, or a partnership system.
Operating principles
The point is not to hide automation behind a personal brand. It is to make the division of labor clear enough that the output can earn trust.
I set the problem, context, standards, and consequence before an agent begins. The system does not invent its own mandate.
Research, contradiction, architecture, implementation, editorial review, and verification are separate passes with different failure conditions.
A strong page lets you distinguish source material, inference, my interpretation, and the decision I made from it.
Broken evidence, generic language, privacy risk, false partnership claims, or weak production proof can stop an otherwise polished release.
Quality is not a final grammar pass. Review can change the thesis, reject the evidence, narrow the claim, or stop the release.
Public outputs
Research
Source-linked investigations across AI systems, models, creator workflows, and emerging technology.
Book intelligence
Books reconstructed as usable models, with Frank’s interpretation kept separate from the source.
Architecture
Reference architectures, agent workflows, evaluation boundaries, and production decisions.
Applied work
Focused briefs, prototypes, and operating systems built around a person, mission, or real workflow.
Field guides
Practical guides that keep the source, decision logic, and next action close to the page.
Working notes
Short dated notes from what Frank is testing, noticing, changing, and learning in public.