AI Agents: Building Autonomous Intelligent Systems
AI Agents: Building Autonomous Intelligent Systems
Master the art and science of building AI agents that can perceive, reason, plan, and act autonomously. This course bridges the gap between language models and intelligent systems that can solve complex real-world problems through tool use, planning, and multi-agent coordination.
Learning Objectives
- Understand agent architectures and autonomous behavior patterns
- Master tool integration and function calling with MCP protocol
- Implement planning algorithms and reasoning strategies
- Design multi-agent systems with coordination and communication
- Build production-ready agents with memory and context management
- Deploy and monitor AI agents in real-world environments
- Apply ethics, safety, and optimization principles to agent systems
Lessons
Foundations of AI Agents: From Reactive to Autonomous
75 minExplore the fundamental concepts of AI agents, their architectures, and the progression from simple reactive systems to sophisticated autonomous agents.
Start Lesson →Agent Architectures: ReAct, Planning, and Reasoning Patterns
75 minDeep dive into modern agent architectures including ReAct (Reasoning + Acting), Plan-and-Execute, and other reasoning patterns that enable intelligent behavior.
Start Lesson →Tool Integration Fundamentals: Function Calling and APIs
60 minLearn how AI agents interact with external tools and APIs through function calling, understanding the mechanisms that extend agent capabilities beyond text generation.
Start Lesson →Advanced Tool Integration: Workflows, Security, and Optimization
90 minMaster advanced tool integration patterns including workflow orchestration, security controls, performance optimization, and production-ready tool systems.
Start Lesson →MCP Protocol and Advanced Tool Orchestration
90 minMaster the Model Context Protocol (MCP) for secure, standardized tool integration, and explore advanced patterns for tool chaining and orchestration.
Start Lesson →Planning Algorithms: Search and Basic Planning
60 minExplore fundamental planning algorithms including breadth-first search, depth-first search, and A* search for goal-oriented agent behavior.
Start Lesson →Advanced Planning: Goal Decomposition and Uncertainty
75 minMaster advanced planning techniques including hierarchical planning, means-ends analysis, and planning under uncertainty.
Start Lesson →Advanced Reasoning: Self-Reflection, Error Recovery, and Adaptation
75 minUnderstand how agents can reason about their own performance, recover from errors, and adapt their strategies through self-reflection and meta-cognition.
Start Lesson →Multi-Agent Systems and Coordination
120 minUnderstand how multiple AI agents can work together, communicate effectively, and coordinate their actions to solve complex problems collaboratively.
Start Lesson →Production Deployment and Operations
120 minLearn comprehensive strategies for deploying AI agents in production, including monitoring, scaling, fault tolerance, and operational best practices.
Start Lesson →Performance Optimization: Efficiency and Application-Level Optimization
90 minLearn techniques for optimizing agent performance, including caching strategies, parallel processing, and application-level efficiency improvements.
Start Lesson →Performance Optimization: Model and Infrastructure Optimization
90 minMaster model quantization, compression, and infrastructure-level optimizations for faster and more cost-effective agent systems.
Start Lesson →Ethics and Safety in AI Agent Systems
90 minExplore ethical frameworks, safety measures, bias detection, and responsible AI practices for building trustworthy agent systems.
Start Lesson →Future Directions and Emerging Trends
90 minExplore the cutting-edge research and emerging trends that will shape the future of AI agent systems and autonomous intelligence.
Start Lesson →