> ## Documentation Index
> Fetch the complete documentation index at: https://aitutorial.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview & Learning Objectives

> Overview and learning objectives for AI Agents

## Module Overview

**You've probably noticed:** Simple LLM calls work great for one-shot tasks, but real applications need systems that can use tools, maintain context, and execute multi-step workflows reliably.

**Here's the challenge:** Building agents that work in demos is easy. Building agents that meet enterprise reliability requirements (95%+ accuracy) is hard. Most agent projects fail not because of the LLM, but because of tool design, memory architecture, and rule enforcement.

**In this module:** You'll build production-grade agent systems — from single-tool agents to multi-server MCP architectures with thread-based memory, security guardrails, and deterministic business rule validation.

## Learning Objectives

By the end of this module, you will be able to:

* ✅ Build agents with tool calling using LangChain's `createAgent`
* ✅ Design and deploy MCP servers with proper tool descriptions
* ✅ Connect agents to multiple MCP servers via `MultiServerMCPClient`
* ✅ Implement thread-based memory with `MemorySaver` and long-term memory patterns
* ✅ Enforce business rules deterministically with validation tools
* ✅ Build security guardrails: PII detection, jailbreak prevention, output filtering
* ✅ Optimize tool selection for accuracy at scale

## Why This Matters

The gap between an agent demo and a production agent is enormous:

* **Tool accuracy:** Agent accuracy drops from 92% to 58% as you go from 5 to 20+ tools. Design matters more than model choice
* **Business rules:** LLMs enforce prompt-based rules \~85% of the time. For financial, legal, or healthcare use cases, that's not enough — deterministic validation is required
* **Security:** Agents with tool access can leak PII, execute destructive actions, or be manipulated via indirect injection. Guardrails are not optional
* **Interoperability:** MCP is the emerging standard for tool integration. Building on it now means your tools work with Claude, ChatGPT, Cursor, and any future MCP client

## What You'll Build

* **Weather agent** — LangChain ReAct agent with tool calling
* **MCP servers** — 3 domain servers (KnowledgeBase, CustomerInfo, IncidentTicket)
* **Customer support agent** — multi-server agent with thread-based sessions and user identity via headers
* **Memory examples** — working memory (MemorySaver) and long-term memory (cross-session persistence)
* **Expense validator** — deterministic business rule enforcement via validation tools
* **Security guardrails** — PII detection/redaction, jailbreak detection, output filtering pipeline
* **Tool analytics** — usage tracking with optimization recommendations