Why does AI still feel so “useless”?(and how to fix it)

I want to share some thoughts on the core concept behind the project we’re building, specifically around the practicality barriers of AI applications, especially agent-based ones.

Right now, compared with model capabilities, the progress of agentic applications in the real market is honestly discouraging. Recent studies (https://arxiv.org/abs/2512.04123v1) also show how poorly agents perform when deployed in real-world settings. The industry’s current obsession is still about pushing agents toward greater complexity and autonomy. That path isn’t wrong, but I don’t believe it explains why agentic applications are failing to gain traction.

In reality, model capabilities today are already strong enough, and most frameworks and infrastructure layers are mature enough (even becoming over-engineered). From a market perspective, we don’t need a perfect, all-powerful agent. We need something that reliably solves a concrete problem and is simple enough for people to actually use.

To me, what’s happening with agent autonomy resembles the blockchain industry’s early pursuit of decentralization. We repeatedly question whether an agent is truly capable of autonomous reasoning and action or merely an automated workflow. To make them look more like “real” agents, we keep piling on components and architectural complexity.

Yes, autonomy is core to the original idea of AI agents, just like decentralization is core to blockchain. But the truth is, most users don’t care. The crypto world has already proven this. Whether the system relies on its own judgment or just follows a preset agent flow, it doesn’t affect its value in the eyes of ordinary users. They only care if it works.

From my own development experience and from testing many community-built open-source agents, it’s clear that focused agents (ones that do one thing only) are genuinely reliable and useful. But the moment we start stuffing more parts into a single agent or a multi-agent system, performance usually drops sharply. Some of the most impressive agents I’ve seen are the simplest and most focused.

A lot of teams I know have already dropped their frameworks and rebuilt their apps from scratch, intentionally limiting agent autonomy. In the end, reliability and stability are the real truths of the market.

This leads me to two conclusions.

First, we should rethink how we view agentic applications. Agents should be treated as capability units, not complex standalone products. This is less obvious in generative apps, but in agent-based systems, the real value comes not from making one agent more powerful but from enabling agents to collaborate seamlessly and in an ecosystem-agnostic way so they can be composed into full, end-to-end services.

Second, if we want agentic applications to become real products, we need a unified layer for packaging and distribution. An agent-composed service must be deliverable as a product that requires zero understanding of the underlying mechanics. This means it must provide unified payments, registration, governance, runtime environments, and frontend interaction. Developers and users shouldn’t have to deal with anything beyond the product’s purpose.

Our solution is to provide an ecosystem-agnostic system layer to wrap agents into standardized executable units with a unified interface. A single runtime handles execution, governance, and capability injection, similar in spirit to a blend of Docker and Android GMS. We firmly believe it can help agentic applications become truly usable and adoptable in the real world.

I’ll pause here due to the length, but I’d love to hear your thoughts and help us validate our direction. And I’m also looking to connect with people who are interested in this topic. I’m more than happy to chat.

I also wrote a longer piece explaining this in more detail. https://charmos.io/blog/1