AI Agent Development with AutoGenin San Francisco

Module 1: Introduction to AI Agents and Agentic AI

• Understand the fundamentals of AI agents and agentic AI systems.
  • Explore how AI agents perceive information, reason about tasks, and take actions.
  • Understand common enterprise and business use cases for AI agents in San Francisco, United States.
• Explore the evolution from traditional AI applications to autonomous agent-based solutions.
  • Understand the role of Large Language Models in modern AI agent development.
  • Identify opportunities for applying agentic AI to real-world workflows.

Module 2: AutoGen Framework Fundamentals

• Understand the architecture and core concepts of AutoGen.
  • Explore agents, messages, conversations, tools, and workflows.
  • Understand how AutoGen can be used to create collaborative AI applications.
• Learn the fundamentals of developing AI agents with Python.
  • Configure the development environment for AutoGen projects.
  • Build a basic AI agent application in San Francisco, United States.

Module 3: LLM Integration for AI Agents

• Learn how to connect AI agents with Large Language Models.
  • Understand model configuration, prompts, context, and response generation.
  • Explore OpenAI-compatible and other supported model integrations.
• Learn how to optimize model usage for agent-based applications.
  • Understand model selection, token usage, latency, and cost considerations.
  • Configure LLM-powered agents for practical applications.

Module 4: Building Conversational AI Agents

• Create conversational agents capable of handling user requests and tasks.
  • Configure agent instructions, roles, system messages, and behavioral guidelines.
  • Design effective conversations for different business scenarios.
• Implement context-aware interactions.
  • Manage conversation history and relevant context.
  • Improve the consistency and usefulness of agent responses in San Francisco, United States.

Module 5: Multi-Agent Systems with AutoGen

• Understand multi-agent architecture and collaborative AI systems.
  • Learn how multiple specialized agents can work together on complex tasks.
  • Define responsibilities and communication patterns between agents.
• Build collaborative multi-agent applications.
  • Create specialized agents for research, analysis, coding, validation, and decision support.
  • Coordinate agents to complete multi-step business workflows.

Module 6: Agent Communication and Collaboration

• Learn how agents exchange information and coordinate activities.
  • Understand message-based communication between AI agents.
  • Design structured communication patterns for collaborative workflows.
• Implement effective agent collaboration.
  • Manage task delegation and information sharing.
  • Reduce unnecessary interactions and improve workflow efficiency.

Module 7: Tool and Function Integration

• Extend AI agent capabilities using external tools and functions.
  • Connect agents with APIs, databases, applications, and custom functions.
  • Configure agents to select and invoke appropriate tools.
• Build tool-enabled AI agents.
  • Create practical tools for calculations, data retrieval, search, and automation.
  • Develop tool-integrated solutions for business use cases in San Francisco, United States.

Module 8: Agent Workflows and Task Orchestration

• Design structured workflows for multi-step AI tasks.
  • Break complex problems into manageable agent tasks.
  • Define task dependencies, execution sequences, and completion criteria.
• Implement agent orchestration strategies.
  • Explore sequential, concurrent, delegation, and collaborative workflow patterns.
  • Select suitable orchestration approaches for different AI applications.

Module 9: AI Agent Planning and Task Decomposition

• Learn how AI agents can plan and execute complex tasks.
  • Decompose user objectives into smaller actionable steps.
  • Define planning instructions and task execution strategies.
• Build planning-oriented agent workflows.
  • Implement task assignment and result validation.
  • Improve the reliability of multi-step agent execution.

Module 10: Agentic RAG and Knowledge Integration

• Understand how Retrieval-Augmented Generation can enhance AI agents.
  • Connect agents with knowledge bases and retrieval systems.
  • Use external information to improve response accuracy and relevance.
• Build agentic knowledge workflows.
  • Integrate document retrieval, search, embeddings, and vector databases.
  • Develop knowledge-enabled AI agents for enterprise use cases in San Francisco, United States.

Module 11: Human-in-the-Loop AI Agents

• Understand the importance of human oversight in agentic AI systems.
  • Identify tasks that require human approval or intervention.
  • Design workflows that allow agents to request human input.
• Implement human-in-the-loop patterns.
  • Add approval and review stages to AI workflows.
  • Balance automation with human decision-making for business applications.

Module 12: Agent Memory and Context Management

• Explore memory and context management for AI agents.
  • Understand short-term conversation context and persistent information.
  • Manage relevant information across multi-step agent interactions.
• Improve agent performance through effective context handling.
  • Reduce irrelevant context and improve information retrieval.
  • Design context-aware agents for practical applications.

Module 13: AI Agent Evaluation and Testing

• Learn methods for evaluating AI agent performance.
  • Define accuracy, relevance, task completion, latency, and reliability metrics.
  • Test individual agents and multi-agent workflows.
• Implement systematic agent testing.
  • Identify incorrect tool calls, poor responses, and workflow failures.
  • Improve agent reliability through iterative testing in San Francisco, United States.

Module 14: Responsible AI and Agent Security

• Understand security and responsible AI considerations for agentic applications.
  • Identify risks related to prompt injection, unauthorized tool usage, and sensitive information.
  • Apply appropriate access controls and validation mechanisms.
• Develop safer AI agent workflows.
  • Implement input validation, output checks, and controlled tool execution.
  • Apply responsible AI practices when deploying agents in organizational environments.

Module 15: Observability and Agent Performance Optimization

• Monitor AI agent execution and workflow behavior.
  • Track agent interactions, tool calls, errors, and task outcomes.
  • Identify bottlenecks and inefficient agent interactions.
• Optimize agent performance.
  • Improve prompts, workflows, tool selection, and model usage.
  • Balance response quality, performance, scalability, and operational cost.

Module 16: Building Production-Ready AI Agent Applications

• Learn considerations for moving AI agents from development to production.
  • Design maintainable agent architectures and reusable components.
  • Configure reliable workflows for real-world applications.
• Explore deployment and operational requirements.
  • Understand scalability, monitoring, security, configuration, and error handling.
  • Prepare AutoGen-based applications for enterprise AI use cases in San Francisco, United States.

Module 17: Advanced Multi-Agent AI Applications

• Develop advanced multi-agent solutions for complex business processes.
  • Combine specialized agents, tools, knowledge sources, and workflows.
  • Implement collaborative problem-solving architectures.
• Explore advanced agentic AI use cases.
  • Build research, customer support, data analysis, software development, and automation agents.
  • Evaluate different architectures for enterprise requirements.

Module 18: Hands-On AI Agent Development Capstone Project

• Build a complete AI agent application using AutoGen.
  • Design the agent architecture and define agent responsibilities.
  • Integrate LLMs, tools, workflows, memory, and knowledge sources.
• Test and optimize the completed application.
  • Evaluate agent performance, security, reliability, and user experience.
  • Deploy and demonstrate a practical AI agent solution for a real-world scenario in San Francisco, United States.