Agentic AI Bootcamp
Build autonomous AI agents capable of complex decision-making.
Duration
8 Weeks
Level
Advanced
About this Course
This bootcamp focuses on the emerging field of Agentic AI, teaching you how to design, build, and deploy autonomous AI agents. You'll learn about planning, memory, tool use, and multi-agent systems to create intelligent agents that can solve complex problems.
What You'll Learn
- Understand the principles and architectures of Agentic AI.
- Design and implement autonomous AI agents using LLMs and frameworks.
- Enable agents to use external tools and manage memory.
- Develop and evaluate multi-agent systems for collaborative tasks.
- Deploy and manage AI agents responsibly.
- Address the ethical and safety challenges of autonomous AI.
Course Modules
Module 1: Introduction to Agentic AI
- What are AI Agents?
- Components of an AI Agent (Perception, Cognition, Action)
- Single-Agent vs. Multi-Agent Systems
- Applications of Agentic AI (Automation, Research, Gaming)
Module 2: Large Language Models (LLMs) for Agents
- Review of LLM capabilities and limitations
- Prompt Engineering for Agentic Behavior
- Fine-tuning LLMs for specific agent tasks
- Integrating LLMs as the 'brain' of an agent
Module 3: Agent Architectures & Frameworks
- Introduction to Agent Frameworks (e.g., LangChain, AutoGPT concepts)
- Designing Agent Loops (Plan, Act, Reflect)
- Memory Management for Agents (Short-term, Long-term, Vector Databases)
- Tool Use and Function Calling
Module 4: Building Single-Agent Systems
- Developing a simple task-oriented agent
- Implementing planning and reasoning modules
- Integrating external APIs and tools (web search, calculators)
- Debugging and evaluating agent performance
Module 5: Multi-Agent Systems & Collaboration
- Designing multi-agent architectures
- Agent communication protocols
- Coordination and negotiation strategies
- Building collaborative AI teams for complex problems
Module 6: Deployment & Ethical Considerations
- Deploying AI Agents in production environments
- Monitoring and maintaining agent systems
- Security considerations for autonomous agents
- Ethical implications of highly autonomous AI (safety, control)
Module 7: Advanced Topics & Capstone Project
- Reinforcement Learning for Agent Control
- Human-Agent Collaboration
- Latest Research in Agentic AI
- Capstone Project: Design and implement a multi-agent system for a real-world problem.
Course Overview
Prerequisites
- Strong Python programming skills, intermediate knowledge of machine learning and LLMs.
Target Audience
- AI/ML Engineers
- Researchers
- Software Developers interested in autonomous systems
- Anyone looking to build advanced AI applications
Technologies Covered
PythonLangChainAutoGPTVector DatabasesLLMsTool Use