Advanced Agent Pipelines
This advanced course takes you deep into the world of autonomous AI agents — systems that can reason, plan, use tools, and collaborate with other agents to accomplish complex goals. You will learn to build multi-agent pipelines using LangChain, LangGraph, and Microsoft AutoGen, integrating real-world tools like web search, code execution, database queries, and file manipulation. By the end, you will have built a fully autonomous research agent and a multi-agent customer service system.
What You'll Learn
Course Curriculum
4 modules • 20 lessons
- What makes an AI agent "autonomous" — the ReAct pattern, tool use, and planning
- LLMs as reasoning engines: How models decide which actions to take
- Agent architectures compared: Single agent vs Multi-agent vs Hierarchical
- Memory systems: Short-term (conversation), long-term (vector stores), and episodic memory
- Setting up your development environment — Python, LangChain, and API keys
- Chains and Agents: Building sequential and dynamic LLM-powered workflows
- Tool integration: Connecting your agent to Google Search, Wikipedia, calculators, and custom APIs
- RAG pipelines: Loading documents, chunking, embedding, and querying with vector databases
- LangGraph: Building stateful agents with cycles, conditions, and human-in-the-loop workflows
- Output parsers and structured data: Getting reliable JSON from LLM responses
- AutoGen fundamentals: AssistantAgent, UserProxyAgent, and GroupChat
- Building a code-writing agent that generates, tests, and iterates on Python scripts
- Multi-agent debates: Creating systems where agents challenge and refine each other's outputs
- Custom agent roles: Planner, Researcher, Coder, Reviewer — orchestrating a software team
- Integrating AutoGen with LangChain tools for the best of both worlds
- Project 1: Autonomous Research Agent — takes a topic, searches the web, synthesizes findings, and produces a report
- Project 2: Multi-Agent Customer Service — a triage agent routes tickets to specialized agents for billing, tech support, and sales
- Project 3: Data Analyst Agent — connects to SQL databases, writes queries, creates visualizations, and explains insights
- Production deployment: Error handling, retry logic, rate limiting, and cost tracking
- Evaluating agent performance: Building test suites and measuring accuracy, latency, and cost
Who Is This For?
Frequently Asked Questions
Yes, Python fundamentals are required. You should be comfortable with functions, classes, pip packages, and basic API calls. We do not teach Python basics in this course.
We primarily use OpenAI GPT-4 and Google Gemini. The code is provider-agnostic, so you can swap in Claude, Mistral, or any other model. Budget $10–20 for API costs during the course.
ChatGPT is a single agent in a chat window. This course teaches you to build systems where multiple specialized agents collaborate, use tools, access databases, and execute code — all autonomously without human prompting.
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No signup required. Dive into the full curriculum above.
Prerequisites
- Python programming fundamentals
- Basic understanding of LLMs and prompt engineering
- Experience with APIs (REST)
- Completion of AI Automation Fundamentals (recommended)
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