Overview

Build robust AI agents. Integrate tools via function calling, generate structured outputs with Pydantic, manage agent state, and utilize short-term and long-term memory. Create data-driven agents that interact with external APIs, search the web, query SQL databases, and perform agentic RAG for dynamic retrieval. Learn to evaluate agent performance for reliable, real-world applications.

Syllabus

Introduction to Building Agents

Get to know your course instructors, set up OpenAI resources, and get an overview of the course.

Extending Agents with Tools

Extend AI agents beyond text with tool integrations, enabling reliable real-time actions and data access.

Building Agents with Tools in Python

Develop AI agents in Python using tools with OpenAI SDK. Interact through language models, build functionality-enhancing tools, and test via tool-augmented exercises.

Structured Outputs

Discover structured outputs in AI: transform responses into actionable JSON for integration. Utilize schemas, parsers, and function calls to enhance reliability and automation in workflows.

Implementing Structured Outputs with Pydantic

Master structured outputs with Pydantic and OpenAI SDK for LLMs. Learn parsing, type validation, and create validated AI agent responses in JSON format.

Agent State Management

Explore agent state management with state machines. Learn how agents track user input, instructions, and tool use for complex workflows, ensuring adaptability and reliability.

Implementing Agent State Management with Python

Master Python state machines: set up environment, define schemas, manage transitions, and run workflows. Explore advanced routing and loops for dynamic workflows.

Short-Term Agent Memory

Explore short-term memory in AI agents, enhancing coherence via state, ephemeral, and ephemeral memory strategies for efficient context retention in active sessions.

Adding Agent Memory with Python

Learn to implement short-term memory in Python for coherent AI interactions via a ChatBot with personas, enabling session continuity and dynamic responses.

External Tools and APIs

Explore using external APIs for real-time data, dynamic actions, and authenticating agents. Discover MCP, a protocol standardizing AI’s tool interoperability and safety.

Integrating External Tools and APIs with OpenAI & Python

Explore using OpenAI and Python to integrate external APIs, make GET/POST/PUT requests, manage API keys, and create agents for real-time data interactions.

Web Search Agents

Equip agents to search web for real-time, unstructured info. Ground responses in evidence using APIs, handle noise, and avoid hallucination for credible answers.

Creating Web Search Agents with Python

Build a web search agent using Python, Tavily API, to integrate real-time web data, parse results, and enhance language models' effectiveness.

Interacting with Databases

Equip agents to access and modify structured data by using SQL for interaction and vector databases for semantic tasks, ensuring seamless integration with private systems.

Building Database Agents in Python

Convert natural language to SQL using SQLAlchemy, SQLite, and text2SQL Agent to interact with databases efficiently through real-world examples and practical applications.

Agentic Retrieval Augmented Generation

Discover Agentic RAG: Enhance RAG by enabling reflection, query reformulation, and intelligent adaption for nuanced answers. Master retrieval, reasoning, and retry loops.

Agentic RAG with Python and ChromaDB

Explore agentic RAG in Python using ChromaDB, integrating AI with retrieval-augmented generation for intelligent document retrieval and processing with OpenAI embeddings.

Long-Term Agent Memory

Explore long-term agent memory: understand semantic, episodic, and procedural memories. Learn storage strategies and best practices for personalized, coherent interactions.

Maintaining Long-Term Agent Memory in Python

Implement long-term memory in Python agents using vector databases for enhanced user interaction, session persistence, and personalized responses.

Agent Evaluation

Agent Evaluation guides assessing an agent’s task completion, quality, tool use, and system metrics using response, step, or trajectory strategies to ensure reliable and efficient operations.

Evaluating Agents with Python

Evaluate Python-based agents by setting environments, creating tools, designing test cases, and using diverse evaluation methods to enhance performance and design.