Lately, while following AI news, I've noticed a pretty obvious shift. Before, when people talked about AI, it was mostly comparing which model is smarter, answers more accurately, or generate
Lately, while following AI news, I've noticed a pretty obvious shift. Before, when people talked about AI, it was mostly comparing which model is smarter, answers more accurately, or generates content faster. But now, more and more AI tools are starting to take on tasks independently—even calling other tools to connect work that used to require human handling. I think this shift is pretty interesting, because AI is slowly going from an assistant that answers questions to a tool that can actually take part in workflows.
I've felt the same way when using AI. Like putting together a market report—you might need to gather sources first, then analyze the data, then translate the content, then pull out the key points. Hand it all to one AI and it probably can't nail every step. But break the task apart, let different AI Agents handle what they're each good at, then stitch the results together at the end—the whole process could be way more efficient.
But this way of collaborating makes me think of another problemIf a large number of AI Agents built by different devs all join the same task in the future, who records and settles how much work each Agent did and how much it should get paid? Once AI actually starts delivering services, these seemingly minor questions could directly affect how the whole ecosystem grows.
That's also why I started following this space—and how I came across LorienCore. It's not trying to build another AI model; the focus is on the settlement infrastructure behind multi-agent collaboration. Put simply, they want a unified rulebook so different AI agents can collaborate on tasks and automatically split service fees based on each one's actual contribution.
I think this approach is actually pretty practical. Just like teamwork IRL—some people do research, some execute, some check the results. AI Agents will likely form the same kind of division of labor down the road. The Agent Settlement Protocol (ASP) from LorienCore aims to connect task invocation, execution records, contribution measurement, and value settlement—giving the whole collaboration process clearer standards.
Take content creation as an example. Say a user needs to put together a research report on some Web3 project — one Agent can handle gathering the material, another Agent does the data analysis, and a third Agent pulls the content together. Under LorienCore's design, these Agents don't all have to come from the same developer. The system can record the execution at each stage and distribute the corresponding fees via stablecoins. That way, developers can focus on building what they're good at, instead of having to build out a whole complex AI service on their own.
What I'm personally watching closely is how it thinks about the developer ecosystem. The AI space isn't short on new tools and apps—but even if a dev builds a solid Agent, finding sustained demand for it isn't easy. LorienCore plans to offer Agent registration, service pricing, call scoring, and revenue management, giving devs a way to turn their technical skills into services other apps can call on. Compared to relying purely on user downloads or subscriptions, this model of providing services based on actual task calls is something I think is worth keeping an eye on.
The design of the project's native token LRIEC is also tied to this ecosystem. Per the whitepaper, the platform mainly uses stablecoins for service settlement between Agents, while LRIEC handles ecosystem incentives, governance participation, and some functional perks. The two have different roles, and I think this division is at least logically clear. That said, whether the token mechanism actually works still depends on real-world usage and ecosystem participation down the road.
Of course, I'm not going to assume a project will succeed just because its direction looks solid. AI Agents are still in a fast-moving phase—multi-agent compatibility, execution quality, data security, and fee settlement all still have real problems to solve. LorienCore is proposing a technical approach worth watching, but whether it can attract developers to integrate, generate real task demand, and keep its settlement mechanism running stably—that's what really determines a project's long-term value.
But looking at where things are heading, I'm still fairly interested in LorienCore. As more and more AI starts taking part in real business, what the market needs might not just be more powerful models — but also infrastructure that lets different agents collaborate, meter, and settle with each other. If AI Agents really do form a network where they call on each other and provide services to each other, then the underlying protocol connecting those services will have its own room to grow.
This is also why I'm willing to keep following LorienCore. Rather than just chasing the AI narrative, I care more about whether a project has found real demand—and how it plans to turn tech into services that can be used long-term. LorienCore's choice of agent-based settlement, at least, shows me another possibility for AI x Web3. Next, I also want to see more progress on developer onboarding, real task scenarios, and ecosystem building.
