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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.

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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

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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

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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