Building Agents

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

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

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

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.

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

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

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

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

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

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

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.

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

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

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.

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

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

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

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

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

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.

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

Congratulations on completing the course!