Full curriculum

Beginner to production — one curriculum.

5 tracks. 40+ in-depth lessons. 30+ runnable agents. Every concept paired with a live demo you can fork. All free.

5
learning tracks
40+
in-depth lessons
30+
runnable agents & swarms
8+
real-world case studies
Learning tracks

A clear path, in order

Start at Track 01 if you're new. Skip ahead if you've shipped with LLMs before — every track stands on its own.

  1. Track 01

    Beginner

    ~3 hours

    Foundations of Generative & Agentic AI

    Start here if you've never built with LLMs. Every concept is explained twice: once like you're 10, once for the engineer in the room.

    What's inside

    • What is a model? (LLM families, base vs instruct, open vs closed)
    • Tokens, context windows, and why they cost money
    • Prompts, system messages, and few-shot patterns
    • Embeddings, vector search, and the retrieval mindset
    • What makes something an "agent" vs a chatbot
    • Glossary of every term you'll see in the wild

    Live templates included

    • First Prompt Lab
    • Token Counter Demo
  2. Track 02

    Beginner → Intermediate

    ~4 hours

    Patterns, Tools & Guardrails

    The seven canonical agentic patterns — wired up live. Tool use, RAG, planner-executor, reflection, routing, parallel fan-out, and HITL approvals.

    What's inside

    • Tool / function calling — the OpenAI schema in plain English
    • Retrieval-Augmented Generation (RAG) with citations
    • Planner → Executor pattern
    • Reflection & self-critique loops
    • Routing & classifier-as-controller
    • Parallel fan-out / map-reduce agents
    • Human-in-the-Loop approvals for risky actions
    • Input/output guardrails: PII, prompt injection, schema validation

    Live templates included

    • Product Support Bot (RAG)
    • Code Reviewer (Tools + Guardrails)
    • Approval Inbox demo
    • Planner-Executor sandbox
  3. Track 03

    Intermediate

    ~3 hours

    Text-to-SQL & Data Agents

    Turn natural language into safe SQL. AST validation, table allow-listing, schema-aware prompting, and the realities of running this in production (Uber QueryGPT-style).

    What's inside

    • Why text-to-SQL is harder than it looks
    • Schema introspection and few-shot grounding
    • AST parsing and validating generated SQL before execution
    • Read-only enforcement and table allow-lists
    • Cost & row-limit guardrails
    • Case study: Uber QueryGPT, Snowflake Cortex Analyst
    • Hands-on: query the SaaS sales lakehouse with English

    Live templates included

    • SQL Analyst Agent
    • RevOps Multi-Agent Swarm
  4. Track 04

    Intermediate → Advanced

    ~4 hours

    Multi-Agent Swarms

    When one agent isn't enough. Build researcher → writer → reviewer pipelines, peer-to-peer collaboration, A2A handoffs, and shared memory.

    What's inside

    • Orchestrator vs peer-to-peer architectures
    • Handoff messages, shared scratchpads, and turn limits
    • A2A (Agent-to-Agent) protocol basics
    • When to split a single agent into a swarm
    • Cost & loop-detection guardrails for swarms
    • Visual swarm canvas — drag, wire, run

    Live templates included

    • Research → Writer → Reviewer swarm
    • Customer Support Triage swarm
    • RevOps SQL Analytics swarm
  5. Track 05

    Advanced

    ~3 hours

    Scaling, Observability & Enterprise

    Production reality: traces, evals, ROI math, security, OpenAI-compatible gateways, multi-provider strategy. Real case studies from Klarna, Uber, Salesforce.

    What's inside

    • Reading execution traces and debugging cost spikes
    • Building your first eval suite
    • Token, latency & cost dashboards
    • AI security: prompt injection, data exfiltration, PII
    • OpenAI-compatible gateways and multi-provider routing
    • ROI formulas and enterprise cost scenarios
    • Maturity model: from prototype to org-wide platform
    • Case studies: Klarna, Uber, Salesforce Agentforce, BMW

    Live templates included

    • Trace Inspector
    • Budget Caps demo
    • Multi-Provider Gateway
What's coming

The curriculum keeps growing

We're adding new tracks every few weeks. Here's what's on deck — vote with your feedback on the contact page.

next

Memory & long-running agents

Episodic and semantic memory patterns, summarization buffers, vector recall — the building blocks for assistants that remember you across sessions.

next

Production guardrails deep-dive

A full track on prompt-injection defense, output validation with structured schemas, and red-teaming your own agents.

soon

Evals & continuous testing

How to write golden datasets, score agent outputs automatically, and gate deployments on regression.

soon

Build-along projects

Multi-day guided projects: ship a customer-support agent, an internal data analyst, and a full RevOps swarm — end-to-end with case-study writeups.

Ready to start Track 01?

Sign up free, no credit card. The whole curriculum is yours.