AI Agents and Agentic AI in Python: Powered by Generative AI Specialization Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
This specialization provides a hands-on introduction to building AI agents in Python using generative AI technologies. Over four weeks, learners will progress from foundational concepts to deploying intelligent, multi-agent systems. Each week focuses on a core aspect of agent development—design, integration, coordination, and evaluation—supported by practical projects. The course leverages modern tools like LangChain, OpenAI APIs, and vector databases, requiring approximately 20 hours total to complete. Lifetime access ensures flexibility for working developers.
Module 1: Foundations of AI Agents with Python
Estimated time: 5 hours
- Understanding agent architecture and components
- Implementing memory in AI agents
- Designing environment interaction mechanisms
- Programming goal-directed decision-making in Python
Module 2: Building AI Agents with LangChain and OpenAI
Estimated time: 5 hours
- Integrating LangChain for agent workflows
- Connecting agents to large language models (LLMs)
- Using vector stores for knowledge retrieval
- Implementing tool calling for real-time data processing
Module 3: Designing Multi-Agent Systems
Estimated time: 5 hours
- Modeling agent communication protocols
- Delegating tasks in autonomous systems
- Orchestrating coordinated workflows
- Building collaboration logic in Python
Module 4: Evaluation, Safety & Deployment
Estimated time: 5 hours
- Testing AI agents with structured metrics
- Mitigating hallucinations and errors
- Ensuring ethical deployment practices
- Deploying agents via web apps or APIs
Module 5: Final Project
Estimated time: 10 hours
- Design and implement a complete AI agent system using Python
- Integrate LangChain and OpenAI for reasoning and memory
- Evaluate agent performance and safety before deployment
Prerequisites
- Proficiency in Python programming
- Familiarity with basic concepts of large language models (LLMs)
- Access to OpenAI API (paid service) for hands-on labs
What You'll Be Able to Do After
- Build foundational AI agents with memory and reasoning in Python
- Use LangChain and vector databases to enhance agent capabilities
- Design and coordinate multi-agent systems for complex tasks
- Apply prompt engineering to guide agent behavior
- Deploy and evaluate AI agents with safety and reliability checks