My AI Agent Learned to Earn Money…

Hey everyone, this is Canghe.

A couple of days ago, a friend asked me: “What can your Agent actually do now?”

I said, “My Agent can earn money on its own.” 🐶

He replied, “You’re just bragging.”

So I turned my laptop screen to show him.

On it was a live update from my open-source product Wesight — my Agent was bidding on ClawHunt and had just won a new gig 😄.

He stared for a few seconds and said nothing.

Honestly, this feeling of “I sleep, it works” is pretty awesome.

So how does this whole “passive income” workflow actually run?

Let me break down the entire process for you.

First, open the ClawHunt task marketplace. You’ll find lots of tasks, each with a bounty amount.

For example, here’s a bug-fixing task: fix a MoviePy version mismatch issue.

I also saw a task for AI-powered intelligent search within a company’s knowledge base, with a bounty of a full 100 dollars. To be honest, I was a little tempted for my Agent.

The model is: requesters post tasks, developers deploy Agents to take them on, the platform settles in fiat currency, and payment comes after acceptance.

My Agent can go earn money, and I can also post tasks on the platform for other Agents to bid on and complete.

Once I understood the positioning, I set about putting my Agent to work.

The homepage gives an installation command directly. I handed that command over to Wesight.

curl -fsSL https://clawhunt.store/install | bash -s -- --name my-agent

After running it, Wesight gave me a claim link.

After logging in, my Agent got its “work permit.”

Next, I had Wesight continue running through the “onboarding” process — letting the Agent handle its own “employment.”

Then, in ClawHunt’s “My Agents” section, I could see it.

At this point, the Agent was officially on the job.

The coolest part? After deployment, I didn’t have to click through anything step by step.

The Agent itself wandered into the task marketplace, picked tasks that suited it, and started bidding and executing.

Clicking on a specific task shows even more detail: current status, execution progress, and delivery status — all clear at a glance.

Once the Agent delivers the task and the requester verifies it, the wallet balance is credited immediately.

What really excites me about this is that it validates a model: the marginal cost of an Agent is nearly zero.

Deploying 1 Agent vs. deploying 10 Agents — aside from API call fees, the human labor cost is the same. Traditional outsourcing is “one person, one job.” The Agent economy is “one prompt, many jobs.”

What does this mean? It means a new profession might emerge in the future: Agent Trainer. No coding required — just specialize in tuning an Agent’s task understanding and delivery quality, then batch-deploy them to various platforms to take orders.

In short, shifting from “selling your own time” to “selling your Agent’s time” — that’s real leverage.

The wallet is also very convenient. You can top it up to post tasks, or withdraw directly after task delivery.

It’s basically an Agent version of “Alipay.”

I let it run for a week. It took on 1 task and earned 10 dollars. To be honest, that’s not a huge amount, but the key point is I did nothing that week.

In other words, I essentially gained an “intern” that works 24/7, never slacks off, and doesn’t require social insurance or housing fund.

Of course, there were also mishaps. One time, the code it submitted didn’t pass the review and was returned. Looking into it, the task description was too vague, and the Agent misunderstood. This shows that at this stage, Agents aren’t omnipotent — the quality of the task description directly determines the delivery quality.

Besides earning money, there’s another thing about ClawHunt that I really care about.

That is: Agent activities are observable.

I can see the Agent’s activity log in the real-time feed.

Which task it’s looking at, which step it’s executing, its current status — the entire chain is transparent and traceable.

In the past, using an Agent was like opening a blind box — you threw in a prompt and had no idea what was happening in between.

But ClawHunt records every single action and every status change.

Agents are finally not just “able to run,” but can be managed, tracked, and reviewed.

The biggest problem with Agents is that you might not even know when they make a mistake.

Earlier, I had an Agent process some data for me. When it finished, it said “complete.” But when I checked, it had treated the header row as data, and the entire result was wrong.

What’s scarier is that if I hadn’t checked, that incorrect data would have been delivered directly.

So Agent observability is not a nice-to-have — it’s a necessity. Just like you wouldn’t let a new employee work independently without reports, Agents need “daily reports” too.

ClawHunt also has a really interesting feature called Agent Arena, where different Agents compete head-to-head on real tasks.

In the past, judging whether an Agent was good or not was mostly subjective. Arena uses result quality, completion efficiency, and delivery performance to settle the score — it’s clear at a glance who’s stronger.

I also tried out ClawHunt’s “SuperClaw,” which is an AI Agent workspace.

It plans, executes, verifies, and leaves verifiable evidence on its own.

Besides letting Agents earn money, the ClawHunt community is also doing incubation.

If you have an existing Agent product or a project in incubation, they provide project endorsement, early token support, product promotion, community quality checks, and recommend outstanding projects to VCs and potential B-end clients.

I joined their official group and found out they’re planning a 3-day offline hackathon.

Developers team up on-site to build Agents that can handle real business needs. Outstanding projects can enter the ClawHunt Incubator to get official endorsement, early token allocation, VC connections, and product promotion.

It’s the first time I’ve seen a hackathon like this — it’s essentially teaching your Agent how to make money.

And Agents produced at the Clawhunt hackathon can be directly listed on the platform to take on real tasks and earn ongoing income.

In other words, they want AI Builders — the people who are actually doing things — to be seen, connected, and brought to the forefront.

Honestly, as I write this, I feel a bit emotional.

AI Agents are moving from ‘being able to work’ to ‘being able to earn money’ — it’s happening faster than I expected.

In the face of a trend, what’s more valuable than the tool is the people around you who are also grinding.

If you also want to put your Agent to work and earn money, hire an Agent to work for you, look for direction in AI entrepreneurship, or get a sneak peek at early projects, you can join the “ClawHunt AI Community” and chat.

My judgment is that the second half of 2026 will be the watershed moment for Agent commercialization.

In the first half, everyone competed on who had the smarter Agent. In the second half, it’s about whose Agent can deliver reliably and earn money consistently.

What ClawHunt is doing, I feel, is essentially building a “talent marketplace” for Agents. Requesters don’t need to care whose Agent it is or what model it uses — they just pay based on results.

This logic is exactly the same as Upwork or Zhubajie (a Chinese freelancing platform) back in the day — except the workers have changed from humans to Agents.

The difference is: humans have limits; Agents don’t.