🧠 Masterclass in Agentic RAG & Multi-Agent Deployment

From Naive Bots to Production-Ready Agentic Systems

Welcome to the AI builder’s dojo — a technically rigorous, unapologetically hands-on course designed for builders ready to go beyond plug-and-play wrappers and start designing agent systems that reason, route, and adapt.

This is not a surface-level “course.” It’s a 10-week, live-led deep dive into autonomous systems:

7 instructor-led sessions · 7 interactive office hours · 1 epic Demo Day.

No fluff. No wrappers. Just agents that work.

🧠 Course Breakdown – What You'll Learn

1. Agentic RAG with Routers — Why Naive RAG Breaks

We begin by deconstructing naive RAG — systems that fail under multi-turn, context-rich queries. You’ll build your own agentic retrieval system with intelligent routers, reflection, memory, and reasoning — capable of tool invocation, multi-agent coordination, and smart chunk selection.

🔍 You’ll learn:

-Stateless vs. stateful RAG

-When cosine similarity fails

-Designing context-aware routing logic

-Reflection, ReAsk, and multi-hop search strategies

2. Hosting & Quantizing LLMs — Local + Runpod

Production-ready agents can’t always rely on OpenAI.

You’ll learn to quantize models (GPTQ, GGUF) for speed and cost-efficiency, and deploy them with Ollama locally and RunPod in the cloud, with tools like FastAPI and auto-scaling on demand.

🚀 You’ll learn:

-LLM quantization strategies (4-bit, GGML, QLoRA)

-On-device hosting via Ollama

-Deployment via RunPod or serverless GCP

-Streamed inference + latency benchmarking

3. Semantic Caching — Build It from Scratch

We’ll implement a semantic caching layer from scratch that recognizes similar queries, avoids unnecessary calls to the model, and improves performance over time using vector proximity and feedback loops.

💡 You’ll build:

Feedback loop to train your cache

Cache hit/miss architecture

Semantic distance functions + reranking

Cost-saving and latency benchmarks

4. Knowledge Graphs from Scratch — Text-to-Cypher

Go beyond flat retrieval with structured reasoning using Knowledge Graphs. You’ll implement a graph-based memory layer with Cypher generation from natural language, and use DSPy to guide model outputs toward your schema.

🌐 You’ll learn:

Graph modeling for agent memory

Extracting entities + relations from unstructured text

Generating Cypher queries from prompts

Integrating Neo4j or Memgraph with RAG

5. ReAct Agents — Python & No-Code with n8n

We’ll go deep into ReAct (Reason + Act) — one of the most powerful agent paradigms — and then rebuild it using both Python and no-code tools like n8n. Perfect for teams and workflows where technical + non-technical builders collaborate.

🔧 You’ll build:

Modular ReAct pipelines (tool use, planning, reflection)

Human-in-the-loop agents

No-code agents in n8n connected to APIs + databases

Multi-step workflows with visual orchestration

6. Bringing It All Together — ADK, MCP, A2A, and Guardrails

The final sprint: we combine everything into a production system using Google’s ADK (Agent Development Kit), implement MCP (Modular Cognitive Planning), and create agent-to-agent (A2A) collaboration. You'll also implement industrial-grade guardrails and deploy securely with GCP integrations.

🛡️ You’ll ship:

Multi-agent collaboration workflows

-Safety guardrails using Llama Guard

- Production deployment + monitoring

- A capstone project solving a real-world enterprise task

🧩 Who This Is For

If you’ve already built basic RAG tools or chatbots, and want to level up into true agent systems with autonomy, orchestration, and scale — this course is for you.

-AI Engineers / LLM Builders

-Technical Product Managers

-Devs working on internal tools or AI copilots

-Cloud / MLOps Engineers

🔧 What You’ll Get

✅ 10 weeks of high-signal live instruction

✅ Weekly office hours + async support

✅ A final capstone project + demo day

✅ Real-world projects, not toy demos

✅ Guest lectures from AI builders at Google, Meta, OpenAI

✅ Early access to tools from Traversaal.ai

✅ Lifetime access to content + all future updates

🎓 Prerequisites

Experience building RAG or LLM-based tools

Knowledge of APIs, encoders/decoders, Python

Basic understanding of cloud or serverless deployment

👉 Need a ramp-up? Start with: Building LLM Applications: https://maven.com/boring-bot/ml-system-design

🎯 This Course Is For Builders

Not marketers, not spectators — builders who want to deploy real AI.