Features
- Build Multi-Agent Automations
- Use Python with LLM
- Prompt Engineering for Agents
- Use RAG and External Data Sources for Agents
Highly recommended. I learned every detail of agents development in this course.
Build Intelligent AI Agents with Python, Large Language Models and Tools
Learn how to develop AI agents that can understand instructions, use tools, process information and perform multi-step tasks. This course introduces the concepts and programming techniques behind agentic AI, helping you progress from understanding AI agents to developing practical Python-based agent applications.
The AI Agents in Python course introduces learners to the principles and development techniques used to build these applications. You will explore how Python connects with large language models (LLMs), how agents use tools and external information, and how multi-step workflows can be designed to solve practical problems.
What You’ll Learn From This Course
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Explain AI agents and how they differ from conventional chatbots and basic LLM applications.
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Describe the main components of an AI agent, including its model, instructions, tools and workflow.
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Design prompts and instructions that guide agent behaviour.
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Develop multi-step workflows that combine reasoning, actions and responses.
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Connect agents to APIs, data sources and other application services.
- Develop a practical AI agent project using Python.
What can you build with AI Agents in Python?
- AI Research Assistant: Design an agent that gathers information from approved sources, organises findings and prepares a summary for review.
- Business Workflow Agent: Explore how agents can coordinate defined tasks, call approved tools and help automate repetitive workflows.
- Customer Support Agent: Explore an assistant that answers questions using approved knowledge sources and routes more complex requests for human review.
Who Should Join?
- Python Developers: Extend existing programming skills into agent-based AI development.
- Aspiring AI Engineers: Build a foundation for developing Python-based AI agent applications.
- Automation Developers: Learn approaches to combining AI models with existing tools and services.
Curriculum
- 11 Sections
- 47 Lessons
- 2 Days
- Module 1: Introduction to AI Agents6 ItemsUnderstand the foundations of agentic AI and the role of autonomous or semi-autonomous systems.
- Module 2: Python Foundations for AI Agents4 ItemsLearn the Python concepts required to develop and integrate AI agent applications.
- Module 3: Large Language Models and Prompt Engineering5 ItemsExplore how LLMs interpret instructions and support agent workflows.
- Module 4: Connecting Python to LLMs3 ItemsLearn how to integrate language models into Python applications.
- Module 5: Tools and Function Calling4 ItemsUnderstand how agents interact with external functions and services.
- Module 6: Building Your First AI Agent4 ItemsApply core concepts to create a simple working agent.
- Module 7: Multi-Step Workflows and Planning4 ItemsExplore how agents can complete tasks that require several actions.
- Module 8: AI Agent Frameworks3 ItemsExplore frameworks that can simplify agent development, subject to the tools selected for the course.
- Module 9: Memory, Knowledge and External Data5 ItemsLearn how agents can use relevant information beyond a single interaction.
- Module 10: Testing, Security and Responsible AI Agents6 ItemsLearn to evaluate agent performance and reduce common risks.
- Module 11: Practical AI Agent Project3 ItemsBring the main course concepts together in a practical application.
Lessons:
