Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that can build itself: 0% → 10:16 - build your first AI agent 25% → 41:05 - master prompt engineering 50% → 54:45 - turn agents into graphs... 75% → 1:20:10 - run loops inside agent graphs 100% → 1:43:33 - build a graph that builds itself Most people build one agent and stop there. Google is teaching everything that comes after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the next one. This 2-hour course is worth more than most paid agent engineering courses. Bookmark it and watch today Then read how to run 1,000 agents from one prompt below ↓show more

a course won't teach you to ship

graph engineering is the future

Does the 1:20:10 section cover running the self-building graph in production?

Single agents are becoming the outdated way to build AI The graph approach is the real unlock

graphs after one agent only help if the first job was already small

the loop section is where graph workflows earn their complexity

2 hours is a commitment ngl

This is really helpful for someone who is starting to build their own AI system. We're doing the exact same thing in our product right now.

The real architectural shift from prompt tweaking to graph engineering is localized failure recovery. In a flat conversational loop, when an agent fails a build or lint step, reprompting pollutes the entire context window and degrades downstream reasoning. With a graph DAG, you can bound recovery loops strictly to the repair node without leaking transient error states to parent nodes. When designing self-building graphs, how do you guard against runaway sub-graph recursion?

Two hours covers the happy path. What happens when the graph tries to build itself and picks a bad schema?

A graph that builds itself still needs someone deciding what counts as progress. I define my own system the same way: the natal chart is character, fixed structure, the luck pillars are fate, the timing layer. Destiny is where the two argue.

@LunarResearcher Going from basic prompting to self-building agent graphs feels like skipping straight from arithmetic to calculus

Starting with one agent and ending with a self-building graph could make the jump beyond single-agent workflows easier to follow

Finally a structured breakdown that makes agent graphs actually approachable for beginners

One agent is the tutorial. The graph is the system. Prompt → agents → graphs → loops → self-building. That’s the syllabus. Most people stop at 10:16. The course starts mattering at 54:45. Free 2 hours from Google beats another $499 agent bootcamp.