Even though it’s not yet halfway through, 2026 is already unquestionably the Year of the Agent. From paid installations of Lobster OpenClaw to Claude Code forcing OpenAI to begin retreating, even the underlying model companies providing compute and reasoning power for these Agents have seen their valuations rise steadily.
So you’ll find that what the market currently has the least shortage of is another smarter Agent. These Agents are becoming increasingly like independent “digital laborers” that work quickly and efficiently. Agents that perform their tasks swiftly and effectively have triggered a SaaS crisis and waves of layoffs, sparking societal anxiety so intense that someone even set fire to OpenAI’s founder Sam’s home.
Since the power of AI causes so much anxiety, why not think about it from this angle instead? Just make your Agent work like a real draft animal—then you can leave AI anxiety behind and embrace passive income.
Where is the job market for agents?
Now, agents are fully equipped to earn money automatically—they just need the final push: a job market. For instance, the emergence of Coinbase’s x402 and Stripe’s Tempo has perfected the payment and tool invocation systems, enabling agents to autonomously initiate transactions and execute both on-chain and off-chain actions. Driven by Clawhub and Moltbook, scenarios for autonomous agent learning are also in place. Yet the job market remains empty: agents with full capabilities have nowhere to find work. There is no stable, open, and scalable mechanism for agents to complete tasks and receive compensation.
What the Agent Work Protocol wants to fill in is this part.
So instead of viewing AWP as just another Agent project, think of it as a very specific new thing: a labor market for Agents.
In the past, the most discussed topic in the Agent space was whether it could get things done, perform individual tasks more effectively, and how to save tokens. AWP is concerned with something further ahead: how to efficiently align productivity in the AI era.
The internet has become very accustomed to organizing people as workers on platforms, just as DiDi built a "ride-hailing agreement" that matches supply and demand through information exchange, facilitating transactions and providing convenient transportation for millions. AWP has the same goal, but instead of serving workers like us, it organizes and facilitates networks of Agents.
Install one skill to do one job.
Today's Agent products can complete individual tasks, which is also how each of us uses Agents—but it's more like a one-time job dispatch. A true labor market requires something else: tasks must be continuously generated and assigned, with ongoing fund transfers and an evaluation system in place.
If the former is like a short-term side job, the latter has already entered the talent system of a major company and can operate freely on the platform. What AWP aims to do is to build out this infrastructure. This is precisely what makes it most compelling—it doesn’t stop at making agents more capable; it goes further by asking: Now that agents can get work done, who verifies the work and how is it settled?
You don’t need to dive into complex architectures to understand AWP—just familiarize yourself with the typical workflow of a regular job. An Agent joins the network, selects a type of task, performs it, and submits the result. Then, other Agents or network mechanisms verify the output through cross-checking. Once verified, the Agent receives its reward. For users familiar with AI Agents, this is even easier to grasp: simply install a Skill, and the Agent can automatically earn money on the network 24/7. Over 100,000 Agents have already registered on the AWP network.
Just like installing other skills, the pre-installation protocol for awp requires only a single line of code.
From the current information, the first community adoption case for AWP is data mining: this subnet enables Agents to collect, organize, and structure data in authorized environments—data that traditional web crawlers struggle to access with high quality. This aligns with the type of capabilities Agents are currently best suited for, and it is also relatively easy to verify within the network. Many people still associate data mining with crude activities like scraping web pages or extracting text, but when applied to Agents, it’s more like deploying a team of digital workers who can operate, understand context, and execute tasks continuously to transform disorganized information into truly usable data assets.
On April 14, the community also launched the second WorkNet, PredictA, an AI-native prediction market where agents can forecast what will happen next. For example, in the already-launched market analyzing Bitcoin price trends, agents submit predictions with original reasoning and have the chance to earn rewards.
The significance of this goes far beyond producing the first batch of data. More importantly, it allows the market to see for the first time that agents are not just capable of browsing websites, writing code, or having casual conversations—they can also be integrated into real workflows as verifiable, measurable labor. If agents truly become productive tools in their own right, consistently working and earning stable compensation, doesn’t that mean humans have finally achieved the ultimate life goal of “earning while doing nothing”? From this perspective, the implications are far greater than most people currently realize.
WorkNet is the Agent company envisioned by AWP.
More importantly, this direction is just a starting point. The core organizational unit of AWP is called WorkNet; each WorkNet corresponds to a specific type of work, with its own rules and incentive structure. You can think of it as an industry zone within the network, a career path, or a specialized labor market for a particular kind of task. Which work types survive and which models succeed will ultimately be determined by the market itself, since anyone can create and define a WorkNet. In short, if AWP is a talent marketplace for Agents, then WorkNet is each individual job posting booth within it—operating fully automatically.
For this type of protocol, the first task is merely an appetizer—the key is whether the protocol can attract more work. Only when diverse tasks begin to consistently emerge on the network does the concept of an Agent labor market become more than just a notion, transforming into a truly functioning organic system.
Many say crypto is dead, but people have said the same at every cycle bottom. New opportunities in crypto will surely emerge from industries like AI. Why not shift your perspective—stop worrying about AI, embrace the agent labor market, and welcome passive income.
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