Agentic Rag: Building a coding agent (no frameworks) 🦄 ep #28

Description

Watch Vaibhav build a coding agent from scratch, showing the differences between traditional RAG and Agentic RAG systems. This live coding session demonstrates why the hardest part isn't the agent loop - it's the tool implementation details that make or break your system. What You'll Learn: • The real difference between deterministic RAG and agent-driven context assembly • Why relative paths vs absolute paths can make or break your grep tool • How to build an agent with 16 tools (glob, grep, read, bash, web search, and more) • Critical context engineering tricks that save thousands of tokens • When you should (and shouldn't) use Agentic RAG in production • Why building from first principles beats using frameworks for learning Key Insights: ✅ 70% of the code was AI-generated, but the crucial 30% required deep understanding ✅ Most time was spent on UI/debugging tools, not the agent logic ✅ Tool implementation details matter more than perfect prompts ✅ Small optimizations (20 tokens saved × 30 calls) compound dramatically Code & Resources: 🔗 Full source code: https://github.com/ai-that-works/ai-that-works/tree/main/2025-10-21-agentic-rag-context-engineering 🔗 BAML Language: https://github.com/BoundaryML/baml 🔗 Discord: https://boundaryml.com/discord Timestamps: 00:00 Introduction to Agentic RAG Systems 02:52 Demo of the Coding Agent 05:44 Understanding Agentic RAG vs Traditional RAG 08:34 Building the Agentic RAG System 11:38 Iterative Development and Testing 14:14 Challenges in Tool Implementation 17:13 Evaluating Tool Effectiveness 19:59 Designing the User Interface for Agents 22:56 Managing State and Context in Agents 26:05 Final Thoughts and Future Improvements 31:07 Navigating Directory Structures in Code 34:16 Optimizing Grep and Read Tools 35:19 Understanding Retrieval-Augmented Generation (RAG) 39:45 Implementing Web Search and Context Efficiency 43:28 Enhancing Tool Interactions and Error Handling 49:13 Iterating on Tool Design and User Experience 55:42 Building Agentic RAG Systems 59:32 Exploring Model Performance and Cost Efficiency 01:00:54 Understanding Tool Responses and Context Engineering 01:01:30 Implementing Feedback Loops in AI Models 01:04:52 Balancing Speed and Accuracy in AI Systems 01:06:46 Designing User Interfaces for AI Interactions 01:08:53 Debugging and Improving AI Pipelines 01:13:24 Dynamic Context Management in AI Systems 01:18:19 Final Thoughts on Building Effective AI Solutions