Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats... the stack. Watch it, then save the framework below.show more

Original video:

Can you please mention his name too? He definitely deserves followers/subscribers for this content.

Added his video below

You did not ask me for my permission to download and upload my video. Please delete and quote my original post. Thanks.

Great found, as always

Thanks, bro! Love your findings too.

This is a great explanation! It breaks down a complex topic into something really understandable.

you are welcome bro, happy its useful for you

this is truly the best explanation of agent loops I've seen

Yeah, mate, this guy definitely made it simple and explained how to build a real structure. By the way, are you building such a system yourself?

Yes, I build such systems myself, but he explains really important things

i'll watch this guide before bed thanks for the alpha

you are welcone buddy. btw are you building harness yourself ?

ai agent loops niche growing so fast to be honest

Agents prompting agents is the future, in my opinion. That’s why it’s growing so fast, bro.

fr fr same as me Movez

crazy how it just keeps fixing itself each run

Yeah, this setup is definitely the next level of engineering.

thanks for sharing!

you are welcome buddy ! btw are you using loops youself ?

just saved this video bro

hope it will be useful for you bro ! really amazing watch

Cool

tracing runs changes everything

Yeah, this is definitely an important part.

Memory matters, but only after you can trust the loop. If you cannot trace and score every run, the memory just stores confusion faster.

loops and agents is the future of AI. thanks for sharing the OG content

Yeah, all three layers matter: loops + harness + evals.

that loop is just the surface, there's the dark matter of agent judgment calls that nobody talks about

Harness, evals, and LLM ops also matter.

Its worth more then $500 bro that's the reason i love x

Yeah, it’s really cool that some engineers are sharing such alpha for free on YouTube.

Ai is a self learning program

Just if you build the correct setup.

The trace-judge-diagnose-fix loop is what actually turns agents from one-shot tools into systems that improve over time. Most of the value in agentic work right now comes from building that feedback infrastructure, not from better single prompts or bigger models.

This video was originally created by @ShenSeanChen and was re-uploaded without his permission. Re-uploading someone else’s original work without permission is copyright infringement, not “sharing”. Please delete this post and quote the original post instead.

@ShenSeanChen

@0xMovez love how you broke it down. feels like i finally get why my AI projects kept stalling. gonna try this loop method next time.

I smell alpha knowledge Not only because he is Chinese

Lol. Yeah, almost every hyped agentic buzzword is simply explained in one watch. Worth booking, mate.

Anyone can easily got understand.

The missing piece is failure taxonomy. Trace and judge loops get far more useful when every bad run is labeled by cause: bad state read, tool misuse, weak plan, missing permission, or impossible task.

20 minutes on agent loops, harness and evals is more useful than most paid courses on this topic

That loop is exactly the agentic flywheel. Trace-judge-fix is the core. For anyone building, the eval harness is where you spend most of your time. Building keryx in public, more on my profile.

Speaking of memory, I stumbled on Atomic Memory recently. Open-source, self-hosted, and built around memory you can actually inspect and edit. Nice to see something that doesn't treat the memory layer as a black box.

The trace-judge-diagnose-fix loop is the part most courses skip because it requires you to already have failing agents to learn from. What's the minimum eval setup someone needs before they run their first real agent in prod — one judge prompt or something more structured?

That trace, judge, fix loop is basically how every good engineer debugs already, just automated now.

agent loops + memory + harness + evals is the real production stack. most people stop at the model. how are you seeing memory fit into the harness in practice?

Best AI lessons come from builders, not expensive courses.

agent loops without evals is just automation agent loops with evals is a system that gets better on its own

Anything on the internet is better than $500 courses. Wikipedia and YouTube are better than $500 courses. Talking to the crazy guy at the gas station about engineering is better than a $500 course.

this is the future but the current failure rate in prod is still too high to hand off completely. getting closer though