This repository describes an architectural model for AI‑assisted development workflows based on Cognitive Task Partitioning.
While the paper itself focuses on architectural principles, the workflows described are intended to be mechanically reproducible in real engineering environments.
This document describes a generic reproducibility model that can be applied to many systems (software services, rule engines, data pipelines, simulations, etc.).
The core idea is that AI‑assisted exploration must ultimately be validated by deterministic tooling that produces verifiable evidence.
The workflow separates two fundamentally different types of work:
Creative Exploration Human reasoning and LLM collaboration are used to generate candidate designs.
Deterministic Verification Automated tooling analyzes, validates, and simulates the artifacts produced during exploration.
The deterministic stages must produce evidence artifacts that can be independently verified.
A reproducible system typically consists of three categories of artifacts:
specification/
implementation/
verification/
Examples include:
Specification artifacts
architecture.md
system_spec.toml
schema.json
Implementation artifacts
source code
rules
config files
Verification artifacts
tests
validation schemas
simulation tooling
The exact structure varies by system type, but the principle remains consistent:
Every artifact produced through exploration must be testable through deterministic tooling.
Purpose: ensure artifacts are well‑formed and internally consistent.
Typical checks include:
Example outputs:
validate_report.md
validation.json
These checks confirm that the artifact structure itself is valid.
Purpose: mechanically analyze system behavior without relying on human reasoning.
Examples of deterministic analysis include:
Outputs typically include:
analysis_report.md
analysis.json
This stage identifies structural problems that may not be obvious from inspection alone.
Purpose: observe emergent behavior of the system under controlled conditions.
Common techniques include:
Example outputs:
test_report.md
metrics.json
traces/
These artifacts capture observable evidence about system behavior.
Human engineers review evidence produced by deterministic systems, rather than relying solely on intuition.
Typical review questions include:
If changes are required, artifacts are modified and the verification pipeline is rerun.
A reproducible system should only move forward when deterministic checks are satisfied.
Typical release requirements include:
This ensures that conclusions about the system are evidence‑based and reproducible.
The key property of this workflow is:
All engineering conclusions must be supported by deterministic evidence.
Human reasoning and AI assistance may generate candidate designs, but release decisions must rely on verifiable analysis and testing.
A complete pipeline typically produces artifacts similar to:
validate_report.md
analysis_report.md
test_report.md
metrics.json
traces/
These outputs provide the evidence layer that supports engineering decisions.
AI systems can generate complex artifacts faster than engineers can reason about their consequences.
Without deterministic verification layers, this can produce systems that are difficult to understand or validate.
Reproducibility ensures that AI‑assisted development remains:
Cognitive Task Partitioning combines human creativity, AI exploration, and deterministic verification into a workflow that preserves traditional engineering rigor while enabling faster design iteration.