AI Agent Frameworks: LangGraph, CrewAI, AutoGen

About this course

This course contains the use of artificial intelligence.

Every team shipping agents this year faces the same moment. The demo worked — then production arrived: a run that looped until the bill landed, state that vanished when a worker restarted, an approval nobody could prove happened, and a trace that explained nothing. The model did not fail. The framework decisions underneath it did.

This course teaches agent frameworks as what they are: the machinery that decides what happens next, where work in progress lives, which tools may act and under whose authority, what evidence a run leaves behind, and whether a 3 a.m. failure becomes a resume, a retry, or a silent loss. You will learn to choose that machinery with a scored, veto-aware selection method — and then to build with each major family until the system survives contact with real users.

LangGraph deeply: state as a typed schema that survives failure, nodes and conditional edges, checkpointing and time travel, interrupts that place a human exactly where an irreversible action needs one, streaming, subgraphs, and a failure clinic. CrewAI deeply: roles, tasks and crews used as contracts, and the sequential, hierarchical and consensual processes priced honestly in tokens, latency and failure modes. The conversational family — AutoGen and the Microsoft Agent Framework — with speaker selection, termination, message budgets and the honest boundary where a conversation should graduate into a graph. And the provider SDKs — OpenAI Agents SDK, Google ADK, Pydantic AI, Semantic Kernel — placed in the landscape with their exit costs named.

Around those pillars, the production disciplines that make any framework survivable: tools designed once and connected through MCP, memory with provenance and quarantine, one trace schema you own, evaluation with golden datasets and regression gates, reliability budgets, cost engineering, and the Course 4 security controls applied to framework machinery.

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