A Chinese developer just explained the shift from Loop Engineering to Graph Engineering better than anyone. most people are still building agents the way that's about to be obsolete. > why single-agent loops break and go "goal blind" > the 4 parts of a graph: nodes, edges, state, policy... > 3 topologies that run everything: diamond, supervisor, pipeline > Anthropic's 5 official workflow patterns the punchline: it's not how many agents you run. it's the determinism you build with verifiers, code fallbacks, and reality anchors. I broke the same architecture down with Kimi K3. Full A-Z guide below.show more

Original link - My private telegram -

Is this dev using AI to make him seem to speak english?

No, bro, I used AI to translate his video to make it easier to understand, and I added subtitles.

So yes then?

love how you broke this down kirill

I just found a good video that explains it and shared it

AI bros rediscovering RDF

the best part is that he explained everything in just 21 mins

I agree, he managed to do it pretty quickly

Unexpected @DavidKPiano reference

Why every thought process has to be labeled with „Engineering“ ?

>not doing AEP hyperlattice engineering >ngmi

goal blind loops miss the policy plus verifier path graphs add

Bro, this is a great video, so I decided to share it and translate it into English

"The punchline: Success is not about running more agents. It comes from adding determinism—verifiers, code fallbacks, and “reality anchors” (hard, checkable evidence like tests or external data) so the probabilistic LLM parts stay grounded. Kirill’s post and follow-ups (including short reaction clips) treat the Chinese explanation as unusually clear and practical, which is why it resonated widely.Broader context. This sits in the mid-2026 wave of AI agent discourse. After Prompt → Context → Harness → Loop Engineering (think persistent single-agent cycles like Ralph loops), the community—sparked in part by comments from OpenClaw creator Peter Steinberger—shifted focus to graphs for multi-agent orchestration. A loop is essentially the simplest possible graph (one node with a self-edge). Graphs become useful once work needs to be specialized, parallelized, or handed off with clear state and verification."

We are reinventing the workflows for creating software every 3 months. This is worse than the times where a new JavaScript framework was released every month.

Most agent systems are basically “run it again and hope it works” with extra steps.

interesting now this has a term / label ?

Graph engineering is the real upgrade here

I agree, it's worth looking into this topic now

Definitely worth exploring further

"Engineer" is a word that is used very lightly these days.

Graph engineering is the real unlock deterministic workflows beat fragile loops every time

This tool uses graph engineering to build faster

The AI dubbing gets in the way of learning but I’ll power through the slop. Thanks bro 😎

@grok summarise into 2 sentences

Welcome to 2023

switch to graph engineering now

I'm also looking into all the possible ways to use it right now, which is why I wrote an article about it

Deterministik sistem gerçekten daha iyi mi Yoksa sadece hataları daha öngörülebilir mi

Everyone draws their own conclusions on this matter. You can't be 100 percent sure.

Kesinlik değil, doğru soruyu bulmak önemli

My work touches agentic W workflow forensics so I get this absolutely...we have to be prepared for stop conditions and having evidence for every traversed edge

Damn I really am falling hook line in sinker for this video, audio and mouth movement in sync ai video. Y'all are good

Thank you for sharing this vid and dubbing the English so well as to make it understandable. Where can we find more videos from “Darfay”? I tried google searching “Best Partners” and Darfay Chinese engineer to find more content from him with no success.

@grok summarize this so I can teach my AI

Graph vs loop framing clicks for me - I hit that "goal blind" wall running long agent loops without realizing it was structural, not a prompting problem. Curious how you handle state when a node needs context from three steps back, not just the last one?

This is a great explanation! Definitely makes you rethink how agents are built. Thanks for shari

Nice one. I think as the video says we have just shifted the perspective and i personally was doing this for quite long now.

It's so funny seeing these terms be (re)invented on a quarterly basis, and every time the previous iteration "just didn't cut it", where they used to say "I run my business like this, it is fully autonomous!" The cynic in me would say it's almost like the term *is* the product.

Strategy games taught me graph thinking beats loop guesswork. State, policy, then execution. Deterministic graphs with verifiers and fallbacks are the only systems I trust for deploying capital.

For clarification, when the speaker says "context", it can refer to two different things. Sometimes it's the background details of a specific situation (typical context) and other times it could mean the working scratch memory for chat nodes. e.g. "The context was corrupted".

Old news.

Does this works well on mobile