4. Scoping Agent Full Code Implementation - Agent 1 | Explained in Tamil | AI Agents | GenAI

In this video I have explained and implemented about the first agent - Scoping Agent of deep research agent. Github link: https://github.com/akash-balakrishnan-22/deep-research-agent Build Deep Research AI Agents from scratch using modern frameworks like LangGraph, LangChain, and Model Context Protocol. This complete series walks you through how LLMs evolve from simple text predictors to autonomous problem solvers, capable of planning, reasoning, researching, and generating structured reports. 🚀 What You’ll Learn * What are Deep Research Agents and how they differ from shallow agents * Why enterprises are moving from chatbots → execution-driven AI systems * Core agent loop: Think → Act → Observe * Orchestration patterns: * Sequential workflows with loops * Graph-based execution (LangGraph) * Key capabilities: * Planning * Memory (short-term + checkpointing) * Tool usage * Autonomy 🧠 Advanced Concepts Covered * Multi-Agent Systems vs Single Agent trade-offs * Context engineering and avoiding context window clashes * Prompt engineering techniques for reliable agents * Using LLM as a Judge for evaluation * Efficient research using tools like Tavily * Parallel vs sequential research strategies ⚡ Game-Changing Optimization * Prompt Caching (Anthropic & Google) * Reduce cost by 50–90% * Faster responses without reprocessing large documents * Context compression for scalable agent pipelines 🏗️ End-to-End Agent Architecture Learn to build a production-ready AI system: Clarification → Brief Generation → Supervisor → Research Agents → Final Report 💡 Why This Matters Traditional chatbots stop at answers.Agents execute tasks. This series helps you build AI systems that can: * Research complex topics * Break down problems * Use tools intelligently * Deliver actionable outputs AI Agents, Deep Research Agents, LangGraph Tutorial, LangChain Agents, Multi-Agent Systems, MCP AI, Prompt Engineering, Context Engineering, LLM Applications, A