AI Agent Developer Specialization Course Syllabus

This specialization provides a hands-on, practical path to mastering AI agent development, covering the full lifecycle from architecture to deployment. With approximately 5 weeks of content, each module takes about one week to complete, combining foundational concepts with real-world projects using LangChain, LLMs, and modern AI tools. Learners will gain experience in building, evaluating, and deploying intelligent agents for automation, chatbots, and copilots.

Module 1: AI Agents Overview and Architecture

Estimated time: 5 hours

• Definition of AI agents
• Agent-environment interaction loop
• Core components: memory, planning, and tools
• Building a basic AI agent with LangChain
• Integrating reasoning and memory in agents

Module 2: Tools & Technologies for AI Agents

Estimated time: 5 hours

• Prompt engineering strategies for agent behavior
• Using vector databases for knowledge retrieval
• Function calling and tool integration
• Retrieval-augmented generation (RAG) with OpenAI API

Module 3: Multi-Agent Systems and Collaboration

Estimated time: 5 hours

• Designing agent communication protocols
• Task delegation among agents
• Orchestration of autonomous workflows
• Building collaborative agent systems with distinct roles

Module 4: Real-World Applications & Deployment

Estimated time: 5 hours

• Developing AI chatbots and coding assistants
• Building AI copilots with external integrations
• Deploying agents using real data and tools

Module 5: Reliability, Evaluation & Safety

Estimated time: 5 hours

• Evaluation metrics for AI agent performance
• Error handling and robustness strategies
• Preventing hallucinations and ensuring safety
• Implementing safeguards in agent outputs

Module 6: Final Project

Estimated time: 10 hours

• Design and deploy a fully functional AI agent
• Incorporate memory, tools, and planning components
• Include evaluation and safety mechanisms

Prerequisites

• Basic knowledge of Python programming
• Familiarity with large language models (LLMs)
• Access to OpenAI or similar LLM APIs (may require subscription)

What You'll Be Able to Do After

• Understand and implement core AI agent architectures
• Build intelligent agents using LangChain and LLMs
• Apply prompt engineering to control agent behavior
• Design and deploy multi-agent collaborative systems
• Integrate, evaluate, and secure AI agents in real-world applications