CLAP Repository and CLAP Commands for Agents

## Details

Join us for an online event dedicated to exploring principles, tools and applications in the domain of Agentic AI and Agentic Large Language Relational Model (LLRM). Whether you're a beginner looking to dive into Agentic AI, this event will have something for everyone. We will discuss the role of Agentic AI and Agentic LLRM in today's world and the importance of various applications. Generative AI uses LLMs to generate text, images, videos along with other contents based on prompts. Agentic AI takes actions making decisions based on reasoning, interacting with external environment in order to reach certain objectives. Agentic AI can operate with a level of autonomy and intelligence based on structured query based meta level languages to add new levels of dimensions for reasoning. Agentic LLRM can make intelligent decisions regarding retrievals from any websites, any external or internal AI models, external/internal data sources along with real-time information retrieval from anywhere in the world to generate accurate and timely results.

Google Meet Link: (Coming Soon)
(Time in Pacific Daylight Time ‎(UTC-7)‎, California (USA) time)
12:00 PM - 12:05 PM Introduction
12:05 PM - 12: 50 PM Session 1

Title: CLAP Paradigm to Natural Language-Driven OS Operations

Speaker: Dr. Shyam S Sarkar, Big Data Science Meetup Organizer

Abstract: There were discussions in past meetups about CLAP (Continuously Learning Agent Platform) repository where certain command types were defined for dealing with AI agents. Each CLAP command is meant for certain type of agent related actions based on prompt(s) or executions of Agentic LLRM-SQL queries.
This meetup event will explore a compelling architectural extension: the same paradigm that powers the Agentic LLRM-SQL system with CTE-based queries and the CLAP repository can be lifted wholesale to become the foundation of a natural language-driven operating system — "CLAP OS" (an **Agentic OS**).

The core insight : in LLRM-SQL, APIs to external and internal data sources are invoked via natural language parameters inside CTE (common table expression) clauses, with similarity-based matching routing queries to the right agent. An operating system is, at its heart, also a collection of APIs — to the file system, scheduler, network stack, memory manager, process manager, device drivers, and security subsystem. If those OS-level APIs are wrapped in the same agent/similarity-matching framework, a user can interact with the entire computing environment through natural language, and the OS kernel becomes an **Agent Kernel**.

12:50 PM - 12:58 PM Q/A