The latest sprint focused on a key milestone in our platform architecture — connecting the Adaptive Schema & Learning Framework (ASLF) to the Reinforced Learning Inference Engine (RLIE).
While the RLIE engine itself is still in early development, this sprint established the foundation for end-to-end communication:
• Job queue and worker – Implemented the rlie_jobs system for durable inference job tracking and asynchronous dispatch.
• Mock RLIE service – Added a lightweight local RLIE mock to simulate job ingestion and responses for integration testing.
• Controller and routes – Extended ASLF controllers to enqueue inference jobs and improved routing for future modular growth.
• Admin and observability tools – Added admin endpoints for job inspection (/v1/admin/rlie/dead) and Prometheus-ready metrics for job states and system health.
• Hardening and documentation – Introduced worker backoff logic, better schema validation, and a full developer README for setup and testing.
With this integration, every ASLF inference request now flows into the job pipeline — queued, dispatched, and tracked — even before the full RLIE core is online. This closes the architectural loop between schema learning and inference, positioning us for next-phase development of true adaptive intelligence within the platform.
— Team GHL
