Enterprise Agentic AI: 100 Labs with LangGraph & FastAPI

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

Enterprise Agentic Workflows is a hands-on course for backend and AI engineers who want to build production-grade multi-agent systems with LangGraph and FastAPI. Across 100 labs you build an async API backbone, stateful agent graphs, MCP tool integrations, multi-agent topologies, and security and observability layers, then deploy the whole system to Kubernetes.

A single agent calling a single tool is a demo. A real enterprise workflow has several agents negotiating a task, a human who needs to approve a risky step, PII that must never reach an external API, and an auditor who wants to see exactly what the system reasoned and why. Getting from “the agent works on my laptop” to a system that survives that scrutiny is a different skill, and it’s the one this course builds.

How the course works

Each of the 100 labs is one focused build with a clear title, such as a StateGraph node, an MCP server, a human-in-the-loop pause, or a Kubernetes manifest. You write the code, run it, and see it work before moving to the next lab. What you’ll work with

Python, FastAPI, async architecture, and Pydantic v2 validation

LangGraph: StateGraph, conditional edges, cyclic graphs, and state reducersSQLite and PostgreSQL checkpointers, time-travel state, and vector-backed memory (Qdrant/Milvus)The Model Context Protocol (MCP), tool binding, and gVisor-sandboxed execution

Multi-agent topologies: supervisor, hierarchical teams, and peer-to-peer networks

Human-in-the-loop interrupts, OpenTelemetry, LangSmith, Prometheus, and GrafanaPrompt-injection defenses, PII masking, RBAC, mTLS, and HashiCorp VaultDocker, Kubernetes, Helm-style manifests, HPA, Stateful

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