Course Goal: To provide learners with a deep understanding of AI Agents, their architecture, core components, and practical implementation using state-of-the-art techniques and frameworks, focusing on the concepts and methodologies outlined in the Google "Agents" learning material.
Prerequisites:
- Completion of "Modern AI Development: From Transformers to Generative Models" or equivalent experience.
- Solid understanding of Transformer architectures and generative models (Diffusion, Flow Matching).
- Advanced proficiency in Python programming.
- Experience with PyTorch and the Hugging Face ecosystem.
- Familiarity with API interactions and basic software development principles.
Course Duration: Approximately 6-8 weeks, with each module taking roughly 1 week.
Tools:
- Python (>= 3.8)
- PyTorch (latest stable version)
- Hugging Face Transformers library
- LangChain library
- (Potentially) Vertex AI platform (depending on access and focus)
- Jupyter Notebooks/Google Colab
- Standard Python libraries (NumPy, Pandas, etc.)
Curriculum Draft:
Module 1: Foundations of AI Agents (Week 1)
- Topic 1.1: Recap of Modern AI and the Need for Agency:
- Brief review of Transformer models and their limitations in real-world interaction.
- Introducing the concept of AI Agents and their role in extending model capabilities.
- Defining agency, autonomy, and goal-oriented behavior in AI systems.
- Connecting the concepts to the limitations discussed in the previous course.
- Topic 1.2: Defining and Understanding AI Agents (Based on Google "Agents"):
- In-depth discussion of the definition of an AI Agent as presented in the Google material.
- Exploring the core capabilities: reasoning, logic, and access to external information.
- Distinguishing between AI Agents and foundational models.
- Topic 1.3: The Agent's Cognitive Architecture:
- Detailed examination of the three essential components: Model, Tools, and Orchestration Layer (as per the Google "Agents" document).
- Understanding the interplay and dependencies between these components.
- Visualizing the agent architecture and its information flow.
- Topic 1.4: Cognitive Architectures in Practice:
- Exploring different cognitive architectures and reasoning frameworks (e.g., ReAct, Chain-of-Thought, Tree-of-Thoughts) as presented in the Google material.
- Analyzing the strengths and weaknesses of each approach.
- Hands-on: Implementing a basic reasoning loop using a chosen framework.
Module 2: Tools for AI Agents: Connecting to the External World (Week 2)
- Topic 2.1: The Role of Tools:
- Understanding why tools are essential for enabling agent interaction with the real world.
- Categorizing different types of tools and their functionalities.
- Connecting tools to the limitations of foundational models.
- Topic 2.2: Extensions: Bridging the API Gap (Based on Google "Agents"):
- Detailed exploration of Extensions as defined in the Google material.
- How Extensions standardize API interactions for agents.
- Understanding the benefits and limitations of using Extensions.
- Hands-on: Building a simple Extension to interact with a public API.
- Topic 2.3: Functions: Granular Control and Client-Side Execution (Based on Google "Agents"):
- In-depth analysis of Functions as presented in the Google material.
- Understanding the scenarios where Functions are preferred over Extensions.
- Exploring the concept of client-side execution and its implications.
- Hands-on: Defining and implementing a Function for a specific task.
- Topic 2.4: Data Stores: Accessing and Utilizing Information (Based on Google "Agents"):
- Detailed examination of Data Stores and their role in providing agents with up-to-date information.
- Understanding vector databases and embeddings for efficient information retrieval.
- Exploring Retrieval Augmented Generation (RAG) as a key application of Data Stores.
- Hands-on: Setting up a basic vector database and implementing a simple RAG pipeline.
Module 3: Implementing and Applying Tools (Week 3)
- Topic 3.1: Building and Integrating Extensions:
- Best practices for designing and developing effective Extensions.
- Handling authentication and authorization within Extensions.
- Error handling and robustness in Extension development.
- Hands-on: Developing a more complex Extension for a practical use case.
- Topic 3.2: Developing and Utilizing Functions:
- Designing Functions for specific tasks and data manipulation.
- Passing data and parameters between the agent and Functions.
- Security considerations when implementing client-side Function execution.
- Hands-on: Building a system that uses Functions to process and format data.
- Topic 3.3: Working with Data Stores in Detail:
- Choosing the right vector database for different needs.
- Strategies for indexing and managing data in vector databases.
- Optimizing retrieval accuracy and efficiency in RAG systems.
- Hands-on: Building a more sophisticated RAG application with data from various sources.
- Topic 3.4: Choosing the Right Tool for the Job:
- Developing a framework for selecting between Extensions, Functions, and Data Stores based on specific requirements.
- Analyzing case studies where different tool types are most effective.
Module 4: Enhancing Agent Performance (Week 4)
- Topic 4.1: In-Context Learning for Agents (Based on Google "Agents"):
- Applying in-context learning techniques to guide agent behavior and tool usage.
- Crafting effective prompts and providing relevant examples.
- Understanding the principles behind successful in-context learning for agents.
- Hands-on: Experimenting with different prompts to influence agent behavior.
- Topic 4.2: Retrieval-Based In-Context Learning (Based on Google "Agents"):
- Implementing retrieval mechanisms to dynamically populate agent prompts with relevant information and examples.
- Building and managing "Example Stores" (as referenced in the Google material).
- Improving agent accuracy and reliability through retrieval-based techniques.
- Hands-on: Integrating a retrieval component into an existing agent.
- Topic 4.3: Fine-tuning for Agent Specialization (Based on Google "Agents"):
- Adapting foundational models for specific agent tasks and tool usage through fine-tuning.
- Preparing training data for effective agent fine-tuning.
- Evaluating the impact of fine-tuning on agent performance.
- Hands-on: Fine-tuning a model for a specific agent use case.
- Topic 4.4: Evaluating and Debugging AI Agents:
- Developing metrics for evaluating agent performance and effectiveness.
- Strategies for debugging complex agent behaviors and tool interactions.
- Understanding common pitfalls and challenges in agent development.
Module 5: Building Agents with Frameworks (Week 5)
- Topic 5.1: Introduction to Agent Frameworks:
- Overview of popular agent frameworks and libraries.
- Focus on LangChain: its architecture, core components, and capabilities (as highlighted in the Google "Agents" document).
- Topic 5.2: Agent Quick Start with LangChain (Based on Google "Agents"):
- Reproducing and analyzing the LangChain example provided in the Google material.
- Understanding the code structure and key functionalities.
- Hands-on: Building and running the LangChain example.
- Topic 5.3: Building Custom Agents with LangChain:
- Designing and implementing custom agent workflows using LangChain.
- Integrating various tools (Extensions, Functions, Data Stores) within a LangChain agent.
- Implementing custom reasoning and orchestration logic.
- Hands-on: Developing a more complex agent using LangChain for a specific application.
- Topic 5.4: Introduction to Vertex AI Agents (Based on Google "Agents"): (Optional, depending on access and focus)
- Overview of Google's Vertex AI platform and its agent-building capabilities.
- Exploring features like Vertex Agent Builder, Vertex Extensions, and Function Calling.
- Understanding the advantages of using a managed platform for agent development.
- (Optional) Hands-on: Exploring the Vertex AI Agents interface and building a basic agent.
Module 6: Advanced Topics and Productionizing AI Agents (Week 6-8, potentially flexible)
- Topic 6.1: Production Applications with Vertex AI Agents (Based on Google "Agents"): (If covered in Module 5)
- Analyzing the architecture of production-ready agents on Vertex AI.
- Discussing the role of natural language interfaces and development tools provided by the platform.
- Topic 6.2: Scaling and Deploying AI Agents:
- Strategies for scaling agent infrastructure and handling high volumes of requests.
- Deployment considerations for different agent architectures.
- Monitoring and maintaining deployed AI Agents.
- Topic 6.3: Agent Chaining and Complex Workflows (Based on Google "Agents"):
- Exploring the concept of combining specialized agents to create complex solutions.
- Designing and implementing multi-agent systems.
- Orchestrating communication and coordination between agents.
- Topic 6.4: Ethical Considerations and Responsible AI Agent Development:
- Addressing potential biases and ethical concerns in AI Agents.
- Strategies for building responsible and trustworthy AI Agent systems.
- Discussing the societal impact of AI Agent technologies.
- Topic 6.5: The Future of AI Agents:
- Emerging trends and research directions in the field of AI Agents.
- Exploring the potential impact of AI Agents across various industries.
- Discussion of the evolving landscape and future possibilities.
- Topic 6.6: Project Presentations and Review:
- Students present their final projects.
- Course wrap-up and discussion of future learning paths.
Assessment:
- Hands-on exercises and coding assignments throughout the modules.
- Quizzes to assess understanding of key concepts and methodologies.
- A significant final project involving the design and implementation of a complex AI Agent system leveraging the learned concepts and tools (potentially using LangChain or exploring Vertex AI).
- Project presentations and demonstrations.
Key Pedagogical Considerations:
- Direct Alignment with Source Material: The curriculum is explicitly designed to follow the structure and content of the Google "Agents" learning material.
- Hands-on Implementation: Emphasis on practical application and coding exercises using LangChain and potentially Vertex AI.
- Real-world Use Cases: Connecting theoretical concepts to practical applications and real-world scenarios.
- Progressive Complexity: Building upon foundational concepts to tackle increasingly complex agent designs and implementations.
- Framework Integration: Deep dive into LangChain as a practical tool for building AI Agents.
- Ethical Awareness: Incorporating discussions on the ethical implications of AI Agent technologies.