2026–
The Deterministic Shell Architecture
Modern AI systems are stochastic at their core. Production systems cannot be. This stack isolates randomness, captures execution signals, and enforces admissible state transitions so automation remains stable under uncertainty.
BrowserAgent — Operator Layer
Executes actions in an unstable environment. All world-facing signals originate here.
- →UI interaction
- →Network navigation
- →Auth flow
- →Tool calls
BrowserState — Signal Capture Layer
Captures execution state as replayable signals. Makes failures observable instead of anecdotal.
- →Cookies / storage
- →Navigation graph
- →Error evidence
- →Reproducible state snapshots
llm-contract — Invariant Boundary
Enforces admissible output states around stochastic model behavior.
- →Schema validation
- →Invariant enforcement
- →Categorized failure modes
- →Targeted repair loop
Stochastic Core, Deterministic Shell
The model remains probabilistic.
The boundary is deterministic.
Execution remains adversarial.
State becomes measurable.
This is the architecture.
Chapters
System Identification
Characterizing LLM behavior as dynamical systems. Inputs, outputs, transfer properties.
Read full chapter →Stochastic Core
Modeling the irreducible randomness in language model outputs. Distributions, not determinism.
Read full chapter →Invariant Boundary
Structural constraints that hold regardless of model, prompt, or context window.
Read full chapter →Zero-Context Architecture
Domain isolation, minimal representations, and reproducible agent behavior.
Read full chapter →Fuzzy Systems
Modeling decision systems under uncertainty through membership activation, rule inference, and output control.
Read full chapter →