Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents. How to go from one agent to a full system that finds leads and makes money: 00:00 - Design an AI agent system 07:38 - Add human handoffs 19:27 - Understand RAG and vector databases... 52:38 - Deploy agents to Google Cloud 1:25:57 - Turn agents into a paid WhatsApp business 1:33:00 - Convert conversations into leads 2:24:01 - Learn loops vs graphs 2:33:08 - Build a multi-tool agent graph Most people are still building AI demos. This course shows the full stack: Agents → RAG → Deployment → Leads → Revenue Building agents is the old workflow. Monetizing agent systems is the new one. This 3-hour watch covers more than most $500 paid courses. Save it before everyone starts selling the same thing.show more

I’m the author of all of these videos. For some reason there’re lots of X accounts using my content without asking me. If you have questions, follow me and let’s chat. Here’s the original post: X: If you want to try AI Agent Harness System with Loop, Memory and Eval, try this hit repo for Waku Agent: Original YouTube channel: I also run a community where I host Q&A sessions live twice a week and will share all of the original system design files: @nikitabier @elonmusk please fix theft on X.

free courses like this are getting better than paid ones

honestly so true

3 hours from agent design to systems that find leads is a useful test of whether the handoffs are actually practical. does the course show the failure cases when rag returns bad context?

yeah failure cases matter

what failure mode are you seeing most—bad research, weak slide structure, or messages that need too much cleanup?

Handoffs are the underrated piece. Single agents demo well but fail in production; a system with humans in the loop is what actually ships.

human handoffs make it work

These are videos from @ShenSeanChen YouTube just stitched together with no direct attribution ? Please post direct links to original source - thanks

publish this on youtube!!

Does this available in YouTube??

Valuable blueprint for revenue focused AI agents.

great blueprint for builders

the youtube thumbnail alone could fund three phd candidates

that thumbnail is wild

the youtube thumbnail alone could fund three phd candidates

Could you please share the Github repo?

Human handoffs and RAG belong in the architecture, not as afterthoughts.

exactly they should be built in

Nice video @ShenSeanChen

real value starts after deployment

thats when it gets real

that's really useful information. i've always known that Google nurtures geniuses

That's a beautiful framework — but the real magic isn't in the vector databases. It's in the moment the agent says "I don't know" and means it.

Is there a skill that turns long form videos into skills

Solid outline, and the ordering is right. Putting human handoffs before RAG saves people a lot of pain later. The part agent courses usually skip is everything the agent needs but is not the agent: accounts, a database you can actually query, an API endpoint, something that runs on a schedule. A demo agent runs when you run it. A useful one has to exist when nobody is watching, and that is a plain backend problem rather than an agent problem.

The biggest opportunity in AI is moving from experiments to execution. Building agents is interesting, but building systems that create real business value is where the money is.

@grok find his the name of the person in the video and his linkedin profile and YouTube id

🙌🫶🏼🙌

the monetization and deployment section really caught my attention

Note100 : 99.5 intelligence long

We’re building ever smarter agents without giving them a computable organisation to execute. That’s the missing layer.

@ezdubs_bot german

The jump from one agent to a system is where the boring failures appear: an agent finds a lead, writes a CRM row, and the API says 200. What proves the right account and fields landed after retries? I’d follow the write with a fresh source read, not just the tool log.

fix my code, make no mistake is all i need

Moving from standalone prompt calls into resilient multi-agent graphs with human handoffs is where AI application development actually creates business value

I wonder if the engineer was able to monetize a lot?

Does this actually book calls or just find leads? AI can't replace SDRs.

The biggest difference between a prototype and a business isn't the model-it's the system around it. A production Al agent needs memory, tool calling, human approval when confidence is low, observability, and continuous evaluation. That's what turns a demo into something people actually pay for.

this engineer is a true genius. i recommend watching it.

I run 12 agents live (a DAX trading system). The hard part was never the LLM — it was keeping them alive: auth expiring every 3h, token races, stale bars. Agent infra is the real moat.