AI Agents for Beginners: Build Agents in Python & OpenAI By Prof. Ryan Ahmed
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AI Agents for Beginners: Build Agents in Python & OpenAI · Program
About this program
AI Agents for Beginners: Build Agents in Python & OpenAI
Online Course
Learning format
AI Agents
Subcategory
English
Course language
$39.99 (list $9.99)
Price
Price may change · updated within 1–2 weeks
Official program page
What you'll learn in AI Agents for Beginners: Build Agents in Python & OpenAI
- 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.
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