Why It’s No Surprise That AI Leaves Many People Disappointed

Understanding the Real Potential of AI Beyond the Hype

In recent times, widespread disappointment with AI has become commonplace, and it’s easy to see why. Much of the media and public discourse is dominated by aggressive marketing from tech entrepreneurs touting superficial solutions—often involving dubious “plastic wrap” approaches to complex issues. This environment feels overwhelming, saturated with fleeting AI trends and endless attempts at monetization, leaving many feeling skeptical about the technology’s true promise.

However, beneath this noise, a quieter revolution is unfolding. Skilled programmers are leveraging AI to develop tailored automation workflows that genuinely streamline their work processes. Unlike the one-size-fits-all products often marketed, these custom solutions are highly adaptable but not necessarily interchangeable or easily transferable. The reality is that we’re not on the cusp of a single, universal AI solution that can do everything—at least, not yet.

The most significant breakthrough in AI isn’t about replacing entire jobs overnight. Instead, it’s about automating the process of automating itself. Typically, complex tasks are broken down into a series of smaller steps—usually five or fewer—each involving a combination of memory systems and API interactions. Think of it as creating an intelligent, multi-layered workflow that can handle intricate processes systematically.

At first glance, this seems straightforward. But the devil is in the details. Each step requires custom prompts, precise sequencing, structured memory integration, and seamless API calls. To orchestrate this, multiple AI agents are needed: one to craft prompts, another to build the workflow architecture—including memory management—and a third to handle external API interactions.

The exciting part? We already have many of these components available. AI models have been capable of generating detailed prompts for some time. In 2023, a notable advancement was introduced through the MCP protocol, which embeds instructions directly into API calls, simplifying communication. Additionally, tools like AgentForge now feature YAML-based architectures that enable AI to independently design entire automation pipelines—from prompt sequencing to memory handling—without requiring users to write a single line of code.

The horizon looks promising. We’re on the verge of an era where automation will become more sophisticated and self-sufficient—all we need to do is wait for these systems to mature. While solving this puzzle isn’t trivial, it might very well mark the last major task humanity automates.

Stay tuned—big changes are coming, and they could redefine how we work and innovate with AI.