SwiftVector wraps stochastic models inside deterministic control loops, then applies a Codex of composable laws and jurisdictions for permissioning, audit trails, and reproducible behavior.
→ State, not prompts, must be the authority
SwiftVector is an architectural pattern that separates stochastic AI reasoning from deterministic system control. By treating AI agents as advisory components within a controlled loop, we achieve reproducibility, auditability, and safety in complex multi-agent systems.
Wrap stochastic AI agents in predictable, reproducible control flows. Every state transition is explicit and auditable.
Agents reason and suggest actions. Reducers hold authority and transform state. Clear boundaries prevent unauthorized behavior.
Every action, every state transition is logged. Replay any execution path for debugging, compliance, or analysis.
Designed for autonomous systems where failure is not acceptable. Deterministic behavior enables formal verification and testing.
SwiftVector shines in scenarios where reliability, auditability, and safety are non-negotiable.
Drones, robotics, and aviation systems require deterministic behavior and complete auditability. SwiftVector provides the architectural foundation for AI-assisted control that remains verifiable and safe.
Generate consistent, branching narratives over thousands of interactions. SwiftVector ensures story state remains coherent and every plot development is traceable and reversible.
Deterministic enforcement layers for autonomous agents. SwiftVector provides the safety kernel that constrains actions, budgets, and permissions while preserving agent capability.
Any application where reproducibility and accountability matter. From medical decision support to financial trading systems, SwiftVector provides the infrastructure for trustworthy AI.
Canonical papers and ongoing writing around SwiftVector.
The core framework specification for deterministic control loops.
The constitutional layer: laws, jurisdictions, and composable constraints for governed autonomy.
Why giving AI agents less freedom makes them more useful. The counterintuitive case for architectural constraints.
Real-time AI inference at the device level. Patterns for deploying intelligence where latency matters.
Forthcoming.
Forthcoming.
Real-world applications built with SwiftVector principles.
AI-assisted ground control station for autonomous drones. Demonstrates SwiftVector in a safety-critical environment where deterministic control is paramount.
AI-powered narrative generation system for long-running RPG campaigns. Shows how SwiftVector maintains story coherence across thousands of state transitions.
Governance layer for OpenClaw that turns SwiftVector law into a deterministic safety kernel.