English | Size: 6.6 GB
Genre: eLearning
Build Real AI Agents with Tools, Memory, Handoffs, & Guardrails Using OpenAI Agents SDK & Python.
What you’ll learn
Build AI agents in Python using OpenAI Agents SDK with Memory and Tools.
Build a travel planner AI Agent that creates an itinerary based on user preferences.
Inspect the agent’s behavior using traces on the OpenAI API platform.
Master best practices in creating Autonomous AI Agents
Master the art of Prompt Engineering & Setting Up Agent Instructions
Understand the Key components of an Effective AI Agent System Prompt (Context, Instruction, Input, & Output Indicator)
Test AI agents and watch it run using the Runner class
Design & build a personal finance AI agent with memory that can recall user preferences, past tasks, & ongoing conversations to deliver personalized experiences
Integrate SQLite-based session storage to enable persistent memory for agents
Test and compare agent behavior before and after adding memory to evaluate improvements in context retention.
Build AI agents that integrate built-in tools (like Code Interpreter) and custom FunctionTools for real-time search & data retrieval
Combine search, memory, and decision-making to run a full end-to-end AI agent workflow.
Build a manager function to orchestrate multi-agent workflows from input to final deliverable.
Implement agent collaboration by passing outputs from one agent to another in a structured workflow.
Create modular AI agents (Researcher, Analyst, Writer) with specialized roles for information gathering, analysis, and reporting.
Implement guardrails to enforce boundaries, such as preventing responses on restricted topics (e.g., politics).
Build handoff mechanisms that transfer context and inputs smoothly between agents
Create a tool as an agent by wrapping an autonomous agent behind a function-tool interface, allowing it to be invoked seamlessly by other agents.
In this course, you’ll learn how to build real AI agents in Python using the OpenAI Agents SDK. You’ll understand the core building blocks of modern AI agent systems, including reasoning, memory, tool usage, planning, and guardrails and how these components work together to automate and enhance tasks.
You will also learn how to develop a team of autonomous AI Agents that can work together to achieve a goal. You’ll learn how to inspect and debug agent behaviour using tracing and observability tools, so your agents are not just powerful, but also transparent, testable, and safe.
By the end of this course, you’ll be able to:
- Build AI agents in Python using the OpenAI Agents SDK
- Understand how agents reason, plan, and execute tasks using system prompts and instructions
- Design effective agent system prompts (context, instructions, inputs, and outputs)
- Understand how AI agents work under the hood
- Use tools, memory, and planning to create more capable, context-aware agents
- Design safe agent interactions with guardrails and controlled execution
- Compare agent behaviour with and without memory to evaluate context retention
This course is designed for developers and engineers who want a practical, code-first introduction to AI agents. No prior experience with agent architectures or machine learning is required just basic Python knowledge and an interest in building real AI systems that work reliably in practice.
Who this course is for:
- Software developers with basic Python skills who want to integrate cutting-edge LLMs and agent frameworks into real-world applications.
- Data scientists, ML engineers, and AI researchers who want to build AI Agents.
- Entrepreneurs and startup Founders wanting to build AI-powered autonomous agents.
- Advanced students and educators looking for practical, hands-on experience with Agentic AI Engineering.
- Corporate innovation teams or R&D teams wanting to prototype AI-powered workflows, assistants, and automations.
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