Research

Background and Foundations

Research captures the conceptual substrate that informs architecture and system design decisions.

Concept

Build a stable body of theory that explains failure modes before implementation choices are made.

stability = f(feedback, observability, boundary_enforcement)

Architecture decisions are guided by control and systems theory, not prompt heuristics.
Model behavior is treated as dynamics to be constrained, measured, and corrected.
Reliability methods can transfer between different AI and automation domains.

Reframing LLM pipelines as open-loop control systems reveals where instability originates and where boundary enforcement must be inserted.

Feedback, state-space dynamics, and transfer-function thinking.

Applying control-theoretic principles to AI architecture and runtime behavior.

Failure surfaces, context limits, and latent-space behavior under uncertainty.