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DevelopmentAdvanced76 lessons50–65 hours
AI Agents from Scratch
Build autonomous AI agents that can browse the web, write code, manage tasks, and work 24/7. The complete deep-dive course. For a faster introduction, see AI Agents Masterclass.
What's Included
- Personal AI coaching agent
- Lifetime access to content
- Student community access
- Completion certificate
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What You'll Learn
Understand agent architecture: perception, reasoning, planning, action, memory
Design and build tools with proper schemas and error handling
Implement memory systems: short-term, long-term, episodic
Build agents with LangGraph, CrewAI, AutoGen, and Claude Agent SDK
Create 4 complete agent projects from research to production deployment
Deploy agents to production with cost management and observability
Implement safety guardrails and evaluation frameworks
Build multi-agent systems with real coordination patterns
Outcomes
- Build production-grade agents with LangGraph, CrewAI, AutoGen, and Claude Agent SDK
- Design agent memory, planning, and tool-use architectures from scratch
- Deploy multi-agent systems that handle real business workflows
- Implement safety guardrails, evaluation, and monitoring for agents
Prerequisites
- -Python proficiency
- -Experience with at least one AI API
- -Understanding of async programming
Projects You'll Build
- Personal research agent with web browsing
- Customer service multi-agent system
- Code generation and review agent
- Multi-agent business automation system
Course Curriculum
Module 1: What Are AI Agents? (Foundations)
- 1.1From chatbots to agents — the evolution of AI applications
- 1.2Build a simple ReAct agent in 30 minutes
- 1.3Agent anatomy: perception, reasoning, planning, action, memory
- 1.4The agent loop: observe, think, act, observe
- 1.5Types of agents: reactive, deliberative, hybrid, autonomous
Module 2: Tool Use Deep Dive
- 2.1Why tools make agents useful
- 2.2Designing tool schemas — names, descriptions, parameters
- 2.3Tool execution: synchronous, async, parallel, conditional
- 2.4Building common tools: web search, database, file ops, API calls
- 2.5Tool error handling: retries, fallbacks, graceful degradation
Module 3: Memory Systems
- 3.1Why agents need memory (beyond the context window)
- 3.2Short-term memory: conversation history and sliding windows
- 3.3Long-term memory: vector databases, knowledge graphs
- 3.4Episodic memory: remembering past experiences and outcomes
- 3.5Memory retrieval: similarity search, recency bias, importance scoring
Module 4: Planning & Reasoning
- 4.1Planning strategies: top-down decomposition, iterative refinement
- 4.2ReAct pattern implementation
- 4.3Plan-and-execute: separate planning from execution
- 4.4Reflection and self-correction
- 4.5Human-in-the-loop: when to ask for help
Module 5: Building Agents with LangGraph
- 5.1LangGraph architecture — states, nodes, edges, conditional routing
- 5.2Your first LangGraph agent
- 5.3State management — passing data through the graph
- 5.4Conditional edges — dynamic routing
- 5.5Checkpointing and persistence
- 5.6LangGraph Studio — visual debugging
Module 6: Building Agents with CrewAI
- 6.1CrewAI philosophy — roles, tasks, and collaborative agents
- 6.2Defining agents with roles, goals, and backstories
- 6.3Task definition and delegation patterns
- 6.4Crew orchestration — sequential, hierarchical, consensual
- 6.5Custom tools and external service integration
Module 7: Building Agents with AutoGen & OpenAI Agents SDK
- 7.1AutoGen's conversation-based approach
- 7.2GroupChat patterns — multiple agents discussing
- 7.3OpenAI Agents SDK — the simplest path for OpenAI-native apps
- 7.4Swarm pattern — lightweight handoff-based coordination
- 7.5Framework comparison matrix — when to use which
Module 8: Claude Agent SDK & Anthropic Ecosystem
- 8.1Claude Agent SDK (TypeScript & Python)
- 8.2Building agents with extended thinking
- 8.3MCP integration — connecting agents to any data source
- 8.4Skills — packaging reusable agent capabilities
- 8.5Subagent delegation — main agent to specialized subagents
Module 9: Project 1 — Personal Research Agent
- 9.1Architecture: research query, web search, document analysis, report
- 9.2Building the search and retrieval layer
- 9.3Source evaluation and credibility scoring
- 9.4Report generation with citations
- 9.5Iterative research: identifying gaps and searching again
Module 10: Project 2 — Customer Service Agent System
- 10.1Multi-agent architecture: router, FAQ, ticket, escalation agents
- 10.2Knowledge base integration
- 10.3Sentiment detection and dynamic routing
- 10.4Escalation logic: when to involve a human
- 10.5Conversation quality monitoring and feedback loops
Module 11: Project 3 — Code Generation & Review Agent
- 11.1Building a coding agent that reads, writes, and executes code
- 11.2Sandboxed code execution
- 11.3Automated testing — agent writes code AND tests
- 11.4Code review agent — analyzing PRs for bugs and security
- 11.5Integration with GitHub/GitLab APIs
Module 12: Production Deployment & Operations
- 12.1Deploying agents — Docker, cloud functions, dedicated servers
- 12.2Cost management — token budgets, caching, model selection
- 12.3Observability — logging, tracing, monitoring
- 12.4Error recovery — graceful failures, retries, circuit breakers
- 12.5Scaling — concurrent agent runs and queue management
Module 13: Safety, Guardrails & Evaluation
- 13.1Why agent safety matters — real examples of agents going wrong
- 13.2Input validation and prompt injection defense
- 13.3Output guardrails — preventing harmful or incorrect outputs
- 13.4Sandboxing — limiting what agents can access
- 13.5Evaluation frameworks — measuring agent performance
Module 14: Project 4 — Multi-Agent Business Automation
- 14.1Architecture: orchestrator, data, analysis, report, notification agents
- 14.2Inter-agent communication protocols
- 14.3Shared state and coordination patterns
- 14.4Failure handling in multi-agent systems
- 14.5End-to-end testing of multi-agent workflows
Module 15: Your Custom Agent (Capstone)
- 15.1Choosing your agent project (guided brainstorming)
- 15.2Architecture design review
- 15.3Implementation sprint with checkpoints
- 15.4Testing, deployment, and documentation
- 15.5Deploy Your Custom Agent to Production and Share a Live Demo
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