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. That’s where AI agents come in. 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 how they’re designed and orchestrated. In this module: You’ll learn to build production-grade agent systems from the ground up. We start with simple single-agent patterns, then explore tool design following MCP best practices, and implement production memory architectures. These foundational concepts prepare you for advanced topics in reliability and multi-agent coordination covered in subsequent modules.Learning Objectives
By the end of this module, you will be able to: ✅ Build agents that can use tools to accomplish tasks✅ Design effective tools following MCP best practices
✅ Implement appropriate memory architecture for agent continuity
✅ Choose between working memory and long-term memory appropriately
✅ Select the right memory integration pattern for your use case
✅ Apply memory strategies for continuous learning
✅ Build agents with both tool capabilities and persistent context