BrainAI System Orchestrator

Deterministic pipeline orchestration wires encoders, workspace, HTM, and symbolic reasoning into a cohesive flow. It specifies explicit boundaries and contracts for pipeline stages, enabling reproducible runs and robust deployment across cognitive modules.

Wiring new cognitive modules requires specifying explicit boundaries and per-module dependencies. You can instantiate the orchestrator with a configuration and run a forward pass on a sample input to verify deterministic orchestration across the newly integrated modules.

PyTorch is required as a dependency to manage module state and execute forward passes. The orchestrator uses it to ensure state management and deterministic execution across the swappable pipeline components.

Feature flags with bypass paths preserve a stable schema and enable robust experimentation. This safe-by-design approach allows you to toggle cognitive modules on or off without breaking the deterministic flow of the pipeline.

Validating contracts and state management involves verifying explicit boundaries for pipeline stages and per-module dependencies. This ensures that refactoring the orchestrator maintains deterministic execution and reproducible runs across all wired components.

Adding telemetry is supported through explicit pipeline boundaries and per-module dependencies. You can integrate telemetry tracking while preserving the stable schema and deterministic orchestration across the cognitive modules.