AI Agent Engineering
Frameworks, Harnesses, and Best Practices
Course Overview
AI Agent Engineering is transforming how we build intelligent systems. When ChatGPT writes code, customer service bots book meetings, or autonomous systems make decisions, there's sophisticated engineering underneath: AI agents that reason, use tools, remember context, and orchestrate complex workflows.
This course takes you from agent fundamentals to production-ready harnesses. You'll master the core frameworks powering modern AI systems—LangChain for agent orchestration and LlamaIndex for retrieval-augmented generation. Through hands-on labs, you'll build real agents, understand production patterns, and learn from case studies like Claude Code and Codex.
Why AI Agent Engineering Matters: As AI systems move from research to production, engineering robust, scalable, and safe agent architectures becomes critical. This course equips you with the technical skills to design multi-agent systems, implement safety controls, manage token budgets, and deploy production harnesses that power real-world applications.
You'll gain practical knowledge of agent architectures, frameworks, and production deployment patterns—no prior AI experience required.
Who Should Attend
No Technical Background Required! This course is designed for anyone interested in understanding and building AI agent systems. Whether you're a developer, product manager, entrepreneur, researcher, or simply curious about AI engineering, you'll gain practical knowledge to work with production AI agents.
Perfect For
- • Software Engineers & Developers
- • Product Managers & Technical Leaders
- • Entrepreneurs & Startup Founders
- • ML Engineers & Data Scientists
- • Researchers & Students
- • Anyone Curious About AI Systems
What You'll Need
- • No coding or ML background required
- • Curiosity about AI systems and intelligent applications
Course Contents
Three comprehensive modules covering foundations to production deployment
Foundations & Frameworks
Build your first AI agent with LangChain
Agent Architecture Fundamentals
- • Tools, memory, planning loops
- • ReAct pattern: Reason → Act → Observe
- • Function calling and tool schemas
LangChain Deep Dive
- • Chains, agents, LCEL (LangChain Expression Language)
- • Memory modules: buffer, summary, vector store
- • Built-in tools vs. custom tool creation
Advanced Frameworks & Components
LlamaIndex for retrieval and Model Context Protocol
LlamaIndex for Retrieval-Augmented Agents
- • Data connectors: PDF, Markdown, GitHub, SQL
- • Indexing strategies: VectorStore, Tree, List
- • Query engines: retrieval, sub-question, router
Advanced Agent Components
- • Skills/tools system: schemas, validation, error handling
- • Memory architectures: semantic, episodic
- • MCP (Model Context Protocol): tool discovery patterns
Production Harnesses & Best Practices
Real-world case studies and harness design
Multi-Agent Orchestration
- • Sub-agents: specialized tasks, parallel execution
- • Delegation patterns: sequential, parallel, hierarchical
- • Token budget management and cost control
Production Engineering Patterns
- • Safety: sandboxing, permission systems
- • Observability: logging, tracing, cost tracking
- • State management: checkpointing, recovery
Case Studies
- • Claude Code: workflow orchestration, MCP integration
- • Codex: multi-model routing, cost optimization
Key Frameworks
LangChain
Agent orchestration framework for building multi-step reasoning systems. Covers chains, agents, and memory modules for production deployments.
LlamaIndex
Retrieval-augmented generation framework for building document Q&A systems. Covers data connectors, indexing strategies, and query engines.
Model Context Protocol (MCP)
Standard protocol for tool discovery and integration in agent systems. Covers dynamic tool registration and server-client architecture patterns.
Claude Code
Production agent harness demonstrating workflow orchestration and permission systems. Case study of real-world multi-agent architecture.
Codex
Multi-model routing and cost optimization strategies for production agent systems. Case study of deployment at scale.
AI Agent Engineering
Frameworks, Harnesses, and Best Practices