The latest sprint focused on a key milestone in our platform architecture \u2014 connecting the Adaptive Schema & Learning Framework (ASLF) to the Reinforced Learning Inference Engine (RLIE).<\/p>\n
While the RLIE engine itself is still in early development, this sprint established the foundation for end-to-end communication:
\n\t\u2022\tJob queue and worker \u2013 Implemented the rlie_jobs system for durable inference job tracking and asynchronous dispatch.
\n\t\u2022\tMock RLIE service \u2013 Added a lightweight local RLIE mock to simulate job ingestion and responses for integration testing.
\n\t\u2022\tController and routes \u2013 Extended ASLF controllers to enqueue inference jobs and improved routing for future modular growth.
\n\t\u2022\tAdmin and observability tools \u2013 Added admin endpoints for job inspection (\/v1\/admin\/rlie\/dead) and Prometheus-ready metrics for job states and system health.
\n\t\u2022\tHardening and documentation \u2013 Introduced worker backoff logic, better schema validation, and a full developer README for setup and testing.<\/p>\n
With this integration, every ASLF inference request now flows into the job pipeline \u2014 queued, dispatched, and tracked \u2014 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.<\/p>\n
\u2014 Team GHL<\/p>\n","protected":false},"excerpt":{"rendered":"
The latest sprint focused on a key milestone in our platform architecture \u2014 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: \u2022 Job queue and worker \u2013 Implemented the rlie_jobs […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-722","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/posts\/722","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/comments?post=722"}],"version-history":[{"count":2,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/posts\/722\/revisions"}],"predecessor-version":[{"id":725,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/posts\/722\/revisions\/725"}],"wp:attachment":[{"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/media?parent=722"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/categories?post=722"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/greyhatlabs.io\/index.php\/wp-json\/wp\/v2\/tags?post=722"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}