It’s easy to see why people feel disappointed with AI advancements.

Understanding the Current State of AI: Beyond the Hype

In recent discussions surrounding artificial intelligence, it’s common for many to feel underwhelmed or skeptical. The reasons are understandable: much of what is showcased in the media or marketed by industry players often appears superficial or driven by profit motives. Clouded by a flood of promotional content from tech entrepreneurs proposing quick-fix solutions—some as flimsy as plastic wrap containing PFAS—it’s easy to dismiss AI as just another overhyped trend.

However, beneath this marketplace noise lies a quieter, more promising development. Skilled programmers and AI researchers are leveraging AI tools behind the scenes to craft tailored automation processes that significantly streamline workflows. Interestingly, these implementations tend to vary widely and often require substantial reconfiguration when adapted from one project to another. This indicates that a universal, all-encompassing AI solution is still a way off.

The real breakthrough in AI isn’t about replacing entire jobs overnight. Instead, it revolves around automating the act of automating itself—a concept that’s both simple in theory and complex in practice. Most automation tasks consist of 1 to 5 interconnected steps, sometimes looping, and all rely on memory systems and interaction with multiple APIs.

At first glance, this might seem straightforward, but each step demands a bespoke prompt, precise sequencing, and a well-structured memory framework integrated into the prompts. Connecting to APIs to perform specific functions further complicates the process, necessitating multiple specialized agents: one to craft prompts, another to manage system architecture—including memory considerations—and a third to interface with APIs.

Fortunately, the foundational tools for this level of automation already exist. AI models have been capable of generating their own prompts for some time, as evidenced by recent research such as the 2023 paper available at https://arxiv.org/abs/2310.08101. Moreover, innovations like the Multi-Chain Protocol (MCP) API now deliver instructions to large language models (LLMs) directly within a standardized framework. Additionally, recent enhancements to tools like AgentForge incorporate YAML-defined architectures, enabling LLMs to autonomously build entire automation systems—sequencing prompts, managing memory, and orchestrating workflows—all without manual coding.

So, what’s next? We are essentially at the cusp of a new era where the automation of automation might become the final frontier in AI development. While solving this challenge is complex, it also signifies that we may soon reach