Understanding the Reasons Behind Common Disappointments with AI

Understanding the Real Potential of AI: Beyond the Hype and Hurdles

In recent discussions, it’s easy to feel disappointed with the current state of AI advancements. Much of the mainstream narrative has been shaped by the influx of aggressive marketing from tech entrepreneurs offering superficial solutions—sometimes described as “plastic wrap” fixes—that address problems many don’t even face. This environment can seem like a chaotic marketplace flooded with shiny, but ultimately unhelpful, AI gimmicks.

However, beneath this noise lies a quieter, more promising story. Skilled developers and programmers are leveraging AI in the background to craft tailored automation processes that significantly simplify their workflows. The challenge is that these solutions tend to be highly specific; they often don’t translate seamlessly from one implementation to another, and broad, universal applications remain out of reach for now.

The true breakthrough in AI won’t simply be replacing entire jobs overnight or tackling tasks in isolation. Instead, the key lies in automating the process of automation itself. This involves orchestrating complex sequences—typically between one and five steps—that may loop or utilize memory systems and interface with various APIs. In essence, we’re talking about automating the creation and management of automation workflows.

At first glance, this sounds straightforward. Yet, in practice, each step demands carefully crafted prompts, correct sequencing, and thoughtfully integrated memory management within prompts. Connecting to external APIs further complicates the process, requiring multiple AI agents: one to generate prompts, another to structure the architecture (including memory handling), and yet another to interact with APIs and pass data appropriately.

The good news is that this ecosystem already exists. AI models have been autonomously generating their own prompts for some time. Recent developments, like the 2023 paper here, introduce the MCP protocol—an innovative API framework that embeds direct instructions for language models (LLMs) within its structure.

Additionally, tools like AgentForge now incorporate YAML-based architectures, empowering AI to independently build comprehensive systems from scratch. These architectures sequence prompts, manage memory, and handle API interactions—all without requiring manual coding.

What does this mean for the future? We simply need to be patient. The challenge of automating complex workflows may be complex, but it’s also the final frontier of automation. Once mastered, it has the potential to fundamentally transform how we work, learn, and innovate—marking the end of a long journey toward fully autonomous AI-driven processes.

Stay tuned—because the