Covering its core concepts, agent orchestration workflows, and essential tools. Design, implement, and manage multi-agent systems, integrate memory and tool-augmented reasoning, and deploy real-world AI agents efficiently. The course emphasizes hands-on, practical applications so you can immediately apply the knowledge to production-grade projects.
The Trainable Cortical Multi-Agent Engine & MCP Co-Processor. Replaces LLM routing with 0-token PyTorch Jacobian gating (-93.7% context), 768-D RSA manifolds, and Offline Dream-RSI self-improvement for Claude, Cursor & Python.
Agentic Rag using LangGraph
A lightweight agentic RAG-style chatbot and Model Context Protocol (MCP) server that answers questions about LangChain using local vector retrieval, real-time `llms.txt` document resolution, and Corrective RAG (CRAG) context quality evaluation. Built with LangChain, Chroma, Ollama, FastMCP, and Streamlit.
Natural langauge to SQL conversation agent.Langsmith for asynchronous trace of evals and logs with token usage from Haiku 4.5 with LangGraph agent on a SQL DB from sqlite3 with Typescript JS frontend and FastAPI middleware. 2025 and 2026 IndIan hotel bookings DB
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