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Google just released a 1-hour course on building agentic knowledge Graphs from scratch: • 00:00 - Introduction to AI agents • 08:09 - Foundations of multi-agent systems • 13:25 - Introduction to Graph engineering • 23:27 - Environment setup for agentic Graphs • 47:43 - Building Graph RAG agents...

197,514 次观看 • 3 个月前 •via X (Twitter)

47 条评论

Jurly 的头像
Jurly3 个月前

the graph engineering section at 13:25 is where the real meat is, skipped the intro

Codez 的头像
Codez3 个月前

yeah. brilliant part thats for sure !

0xRafy 的头像
0xRafy3 个月前

Graphs are goat, its another step in engineering

Codez 的头像
Codez3 个月前

yeah, graphs are the future of multi-agentic systems !

ISOfunds 的头像
ISOfunds3 个月前

agent memory banks via graphs are the only way to prevent context collapse in complex tasks. glad the industry is finally moving past basic vector search

Codez 的头像
Codez3 个月前

Yeah. Agree on this

Elshayib 的头像
Elshayib3 个月前

When are they gonna release a model tho ?lol

Codez 的头像
Codez3 个月前

Lol. I wish i know bro !

Morty 的头像
Morty3 个月前

Nice share Codez, thanks

Codez 的头像
Codez3 个月前

Thx Morty. Happy it’s catched your attention

Asymet 的头像
Asymet3 个月前

One hour that actually looks worth saving.

Codez 的头像
Codez3 个月前

1-hour that will change the way you are building agents forever

Argona 的头像
Argona3 个月前

graph memory is the one piece I still havent got right my notes just sit in flat files never linking to anything, watching this tonight

rewind 的头像
rewind3 个月前

loops were only the beginning of orchestration

Gipp 🦅 的头像
Gipp 🦅3 个月前

that section at 47:43 on building graph rag agents is what i needed

Codez 的头像
Codez3 个月前

yeah, the real workshop by google team

Lunar 的头像
Lunar3 个月前

graphs are where agent memory starts becoming architecture

Codez 的头像
Codez3 个月前

yeah, huge setp for building multi-agentic systems in my opinion

starmex 的头像
starmex3 个月前

bookmarked, ty for the drop

Codez 的头像
Codez3 个月前

You’re welcome, buddy! Have you read this article on graph engineering yet? It’s top-tier writing.

starmex 的头像
starmex3 个月前

already done!

dunik 的头像
dunik3 个月前

very alpha video! thank you, Codez! book + like!

Codez 的头像
Codez3 个月前

You are welcome Dunik. Have you started to build your agentic graphs ?

Hussain Hashim | Building SundayBack 的头像
Hussain Hashim | Building SundayBack3 个月前

@0xCodez legit never thought I'd be interested in Graph engineering, but this has me hooked. Gonna check it out tonight!

0xSlyth 的头像
0xSlyth3 个月前

graphs are the future of agent design

Codez 的头像
Codez3 个月前

Yeah, it’s basically the layer of multi-agent orchestration.

Fabrizio Degeno 的头像
Fabrizio Degeno3 个月前

A new wave

Codez 的头像
Codez3 个月前

Graph engineering is the future of AI agents.

Fabrizio Degeno 的头像
Fabrizio Degeno3 个月前

Yeah, it's the logical next step

RH Fardin 的头像
RH Fardin3 个月前

The jump from one giant hallucinating while-loop to explicit graphs is where agents become debuggable. Solid curriculum.

Money Bunny 的头像
Money Bunny3 个月前

graphs turned my scattered AI knowledge into one connected mind

Rahul Pahuja 的头像
Rahul Pahuja3 个月前

nice

Marshall 的头像
Marshall3 个月前

Or.. you could just ask your Agent to do it 🤷🏼‍♂️

Silence 的头像
Silence3 个月前

bookmarked same shelf as the other five 'from scratch' courses i swore i'd finish

nyk 的头像
nyk3 个月前

That chapter order gets the hard part right: graph engineering before the build. In practice, durable state and idempotent nodes matter more than adding agents once retries start.

hub git 的头像
hub git3 个月前

Good

灰藤健吾 │ 「AIで加速する効率化の教科書」著者 的头像
灰藤健吾 │ 「AIで加速する効率化の教科書」著者3 个月前

@grok これの元動画探して。ぽすとでもいいし、YouTubeあるならそれを優先で

AI Mastery Guide 的头像
AI Mastery Guide3 个月前

A full free course on knowledge graphs, that's a solid find

FoxyPenguin 小狐狸 的头像
FoxyPenguin 小狐狸3 个月前

Incredible, thank you for sharing!

ViceSol 的头像
ViceSol3 个月前

Knowledge graphs become much more useful when they're connected to agents that can reason over them, not just store information

Hussain Fakhruddin 的头像
Hussain Fakhruddin3 个月前

Free course like this is a solid place to start.

WANGDY 的头像
WANGDY3 个月前

Nice, 1 hour well spent. Agentic knowledge graphs are where RAG meets real autonomy — curious how they handle graph updates without re-indexing everything.

Yumzlef 的头像
Yumzlef3 个月前

This is exactly the kind of AI insight that separates the serious builders

Shoopy 的头像
Shoopy3 个月前

Bookmarked

Zumer 的头像
Zumer3 个月前

Knowledge graphs are becoming a key skill for future agents.

Granite 的头像
Granite3 个月前

decent video gotta check out what other lectures this girl's posted

AI Apps API 的头像
AI Apps API3 个月前

The while-loop-with-a-growing-prompt failure mode is the right thing to lead with, and it is worth naming why it happens: a loop has nowhere to put state except the prompt, so every step ends up paying for every step before it. Splitting into a graph fixes the context growth. What catches people next is durability. Nodes fail, a tool times out, a model returns malformed JSON, and if the graph only lives in process memory the whole run dies with it. Once each node can checkpoint and be retried on its own, the graph stops being a diagram and starts being something you can leave running unattended.

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