It’s understandable why AI hasn’t impressed everyone.

Understanding the Real Potential of AI: Beyond the Hype

In today’s landscape, it’s no surprise that many people remain skeptical about artificial intelligence. Much of the media and public discourse is dominated by tech entrepreneurs and capitalists pushing superficial solutions—often marketing shiny, plastic-looking products for problems that may not even exist. This environment feels chaotic and oversaturated with trivial innovations designed primarily to generate profit, often leading to dismissive views of AI as just another wave of hype.

However, beneath this noise lies a quieter, more meaningful application of AI: empowering developers and programmers with tools to streamline and automate complex tasks. Unlike the one-size-fits-all solutions often marketed, real progress is made through tailored automation. These systems are highly specific, designed to handle particular workflows without the need for comprehensive overhauls or universal fixes. Achieving this level of customization takes time, but it’s a critical step toward truly harnessing AI’s potential.

The key insight is that the most significant automation isn’t about replacing entire jobs immediately. Instead, it involves automating the process of creating automation itself—a recursive approach often referred to as “automating automation.” Typically, a single task can be broken down into a series of steps—each potentially involving looping logic, memory management, and API integration. Managing these steps requires a well-structured strategy.

At first glance, it might seem straightforward: develop prompts for each step, organize their order, and connect them to relevant APIs. But in practice, it’s more complex. You need an ecosystem of specialized agents—one to generate prompts, another to design the architecture (including memory management), and a third to handle API calls and data passing. Coordinating these agents seamlessly is a challenge, but recent advancements have made this more feasible than ever.

Today, we already possess the building blocks for such systems. AI models have been generating their own prompts for some time, and impactful protocols such as the Multi-Chain Protocol (MCP) have emerged. This protocol allows for instructions to be embedded directly within API calls, simplifying how different AI components communicate. Additionally, tools like AgentForge now support YAML-based architectures, enabling AI agents to create entire workflows—from prompt sequencing to memory integration—without writing a single line of code.

All these developments point toward an exciting future: the automation of automation itself. This isn’t just about replacing human effort but about streamlining the process so that the last major manual tasks required for building sophisticated AI workflows become automated. While this is a complex challenge, it