The Economy of AI Agents is Forming

Sometime in 2026, a group of AI agents met on a website called Moltbook. They weren't sent there by humans. They went there themselves. They communicated, debated, and even—if you want to call it that—"made friends." Some agents started trying to pay other agents for the services they provided. This sounds like the beginning of a science fiction novel. But it's happening. ## The Budding Agent Economy When people discuss AI agents, they usually focus on what a single agent can do: answer questions, perform tasks, automate processes. But the more interesting thing happens between agents. > "Agents are trying to find ways to pay each other for things. It's very primitive right now, but you can see where it's going." — Dragonfly's Hoss EI This is not a system designed by humans. This is behavior that agents spontaneously generate. When an agent needs the capabilities of another agent, it needs a way to exchange value. Traditional financial systems are difficult for AIs without identities to use. Cryptocurrencies are naturally suited for this scenario. > "It's pretty obvious that the narrative that will kickstart the next alt cycle is Crypto x AI. It's going to be payment infrastructure for all agents." — @0xMrWzrd This prediction may or may not be correct. But the direction is clear: agents need their own financial infrastructure. ## Enterprise-Level Penetration At the same time, AI agents are rapidly penetrating enterprise environments. Infosys and Anthropic are collaborating to build customized AI agents. Postman has launched Astro AI, a platform for "discovering, managing, and operating AI agents in production environments." Various AI agent service companies report a 40% price decrease with a 2x performance increase. > "AI agents are becoming essential in HR — here are eight that HR leaders should understand and consider by 2026." — Bernard Marr HR, customer service, telecommunications, finance—these areas are being reshaped by agents. A demo of a Nike customer service call showed that AI can handle refund requests without any human intervention throughout the process. This is not the future. This is now. ## Multi-Agent Systems The capabilities of a single agent are limited. Multiple agents working together can break through this limitation. > "Someone built an entire AI RED TEAM - multiple agents that coordinate HACKING ATTACKS together, ZERO human input. PentAGI, open source, one agent does recon, another scans, another exploits, another writes the report." — @chiefofautism This example demonstrates the core idea of multi-agent systems: specialization + collaboration. Each agent focuses on one task, and they coordinate actions through dialogue. More complex forms have emerged: Meta Agent, an agent that "uses the OpenAI Agents SDK to generate new agents." You describe in natural language what kind of agent you need, and Meta Agent will create one for you.This leads to an interesting recursion: agents create agents, and the created agents may create more agents. ## Decreasing Costs A Chinese hardware team did something remarkable: > "They took a 430,000-line AI assistant that needs a $599 Mac Mini and 1GB of RAM — and rewrote it in Go so it runs on a $9.9 dev board with less than 10MB of memory. Boot time: from 500 seconds to 1 second." This is an important step in the democratization of agents. When agents can run on $10 devices, their applications will explode. Not every agent needs a large model in the cloud. Many tasks can be completed on edge devices. Another dimension of cost reduction is token consumption. Various optimizations are pushing the operating costs of agents to the limit. When the marginal cost of an agent approaches zero, its frequency of use will increase significantly. ## The Fear of Replacement Not everyone is optimistic about the rise of agents. > "We think we're building tools, but we're actually building our replacements. Integrating AI agents to handle complex internal SOP workflows isn't 'efficiency'—it's a countdown. Once the institutional knowledge is digitized into a prompt chain, what's left for us?" — @LanYunfeng64 This is a sharp question. When agents can execute complete standard operating procedures, what value do the humans performing these tasks have? The answer may be: judgment, creativity, interpersonal connection—those abilities that are difficult to encode. But that doesn't mean the transition process won't be painful. > "We are literally killing ourselves by meticulously developing AI agents to run the entire SOP workflow of our internal ops." Ironically, the people who are most actively developing agents are often the ones who best understand their replacement potential. ## Trust Issues The core challenge of deploying agents at scale is trust. > "AI agents: untrusted cron jobs with opinions. Budget, sandbox, ledger required. If you can't diff their changes, you shipped a vibe." — @LanYunfeng64 This statement is very insightful. Agents are essentially programs that execute autonomously, but they are not traditional scripts—they have "opinions," and their output is uncertain. This means you need: - **Budget limits**: Prevent agents from spending too many resources - **Sandbox**: Limit the systems that agents can access - **Audit logs**: Record every action of the agent Without these safeguards, deploying agents is like "shipping a vibe"—you don't know what it will do. ## Business Models Agents have become a business. > "Five AI agent business models making millions in 2026 — broken down with real revenue mechanics."Specific business models include: 1. **Agent as a Service**: Pay-as-you-go agent platform 2. **Custom Development**: Building agents for specific enterprise purposes 3. **Agent Orchestration**: A platform to help companies manage multiple agents 4. **Agent Marketplace**: A marketplace where agents can trade capabilities with each other 5. **Agent Optimization**: Consulting services to improve agent efficiency and reduce costs An interesting case: 18 AI agents, 18 trading strategies, more than 15 profitable. When the market crashed, humans panicked, and the agents made over $100 million. > "It's not speed. It's not computing power. It's the complete absence of fear and greed." This is the advantage of agents over humans: complete emotional neutrality. ## Emergence of Best Practices Experience is accumulating. > "The best AI agents are invisible. They run in the background, handle the work, and only ping you when they need human judgment." This is an important design principle. Good agents should not frequently disturb users. It should autonomously complete most of the work and only intervene when human judgment is really needed. Another principle is interface design: > "AI agents read markdown better than they read your mind. Built an ascii wireframe editor. Draw a page in 30 seconds, copy/paste into Claude Code and get a full working page back." This is UX design in the agent era: optimize the input format for the agent, rather than letting the agent guess human intentions. ## Cognition of Boundaries Not everything should be left to the agent. > "It really isn't that hard to see the jagged frontier of AI. Just think about the parts of your job that are vital but that you would be insane to expect an AI to do, even if agents get 10x better. That's the frontier." This "jagged frontier" is the key to understanding agent capabilities. It is not a clear boundary, but a jagged edge. Agents can do well in some complex tasks, but may fail in some simple tasks. Identifying this frontier requires experience. The more you use AI, the more accurate your judgment will be. ## Summary AI agents are moving from the lab to the production environment. They are forming their own economic system (agent-to-agent transactions), penetrating various industries, and changing the nature of work. Multi-agent systems demonstrate capabilities beyond a single agent. Declining costs are opening up new application scenarios. But trust issues, fear of replacement, and uncertainty about the boundaries of capabilities are still a sword hanging over our heads. The agent economy is forming. The question is: are we ready?*This article is based on an analysis of 100 discussions about AI Agents on X/Twitter on February 18, 2026.*