Architecturally Shallow

Current frameworks lack integrated memory, planning, and learning structures required for sophisticated, real-world tasks.

Fundamentally Unreliable

Plagued by hallucinations and unstable coordination, they remain unfit for mission-critical applications where trust is non-negotiable.

Built for a Monolithic Past

Inflexible, monolithic designs cannot orchestrate workloads across the coming explosion of diverse AI models and intelligent devices.

Self-Evolving

Detects anomalies, reasons about failures, and triggers "mutations" like retraining to continuously adapt and improve.

Provably Stable

Anchored in the Fidelity principle, a canonical energy model ensures stability and prevents chaotic behavior.

Smart & Efficient

Handles 90% of tasks on a sub-80ms fast path, reserving costly deep reasoning for when it's truly required.

Distributed Native

Built for the modern world of cloud data centers, industrial fleets, and swarms of intelligent robots.

Fidelity

Every control signal is a projection of the organism’s canonical energy state. This ensures decisions are grounded, auditable, and mathematically stable.

Performance

Fast path ensures sub-second trust; the flywheel compounds knowledge off-path for continual growth.

Safety & Cost

A multi-layered defense with configurable guardrails, resource budgets, and an auditable lifecycle ensures mutations are always beneficial and bounded.

Fidelity

• s_drift: A canonical novelty score that gates fast vs. deep reasoning.
• ΔE: Mutations (retrain, tune) only occur when the expected energy benefit exceeds the cost.
• Predicate Routing: Simple YAML-based rules are tied directly to the canonical system state.

Performance

• Fast Path: Ensures sub-second trust with multi-tier caches (L0/L1/L2) and single-flight guards to prevent stampedes.
• Knowledge Flywheel: Compounds knowledge off-path for continual growth through memory synthesis, model tuning, and telemetry.

Safety & Cost

• Predicate Router: A configurable guardrail that gates all high-cost actions based on declarative policies.
• GPU Guard: Enforces hard concurrency limits and daily GPU budgets to control spend.
• Auditable Lifecycle: Tracks ΔE_est → E_before → ΔE_realized → cost for every mutation.

Meta-Controller via Reinforcement Learning

The Coordinator can evolve into an RL agent—optimizing its own routing and mutation policies against a long-term reward signal that balances task success, latency, and energy gain.

Defense-in-Depth Security

A multi-layered security architecture provides comprehensive protection, from blocking anomalous inputs at the edge to executing high-stakes tasks in cryptographically secured environments.