Building Agents

Übersicht

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.

Lehrplan

• 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 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.
• Course Conclusion
Congratulations on completing the course!
• UdaPlay - An AI Research Agent for the Video Game Industry
In this project, students will build a stateful AI Research Agent designed to explore the video game industry.

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Unterrichtet von

Henrique Santana

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Fachgebiete

Computer Science