It’s natural to feel disappointed with AI developments.

Reevaluating AI Progress: Beyond the Hype Toward True Automation

It’s no surprise that public perception of AI often feels underwhelming. Much of the discourse is dominated by entrepreneurial tech enthusiasts promoting simplistic, often superficial solutions—what some might call “plastic wrap” fixes—for problems that don’t truly exist. This environment, driven by relentless commercialization, can seem like a chaotic landscape of gimmicks and empty promises. In the midst of this noise, genuine innovation often goes unnoticed.

Behind the scenes, however, experienced developers and engineers are leveraging AI in more meaningful ways. They’re building tailored automation systems to streamline complex workflows and reduce manual effort. These implementations tend to be highly specialized, often not easily transferable or scalable across different tasks. A universal, one-size-fits-all AI solution remains elusive—at least for now.

The real breakthrough isn’t about automating entire jobs overnight. Instead, it’s about automating the process of automation itself. By focusing on automating individual tasks—each consisting of a handful of steps that may involve looping, memory, and API interactions—we come closer to mimicking human efficiency. Typically, automating a task involves several interconnected components: prompting systems for instruction, architecture builders to organize memory, and API agents to handle external data exchanges.

While this might sound straightforward, the complexity lies in integrating these components seamlessly. Custom prompts need to be crafted and ordered precisely, memory systems must be structured and embedded within prompts, and the connectors to external services must be robust and flexible. This often requires multiple AI agents working together—one to generate prompts, another to organize the architecture, and a third to manage API communications.

Fortunately, much of this infrastructure is already in place. AI models have been autonomously generating their own prompts for some time. Recent developments like the MCP protocol—a standardized API that embeds instructions directly within its framework—are paving the way for more integrated, autonomous systems. Additionally, tools like AgentForge now allow for YAML-defined architectures, enabling AI to design and execute complex workflows from scratch, seamlessly sequencing prompts, managing memory, and communicating with multiple APIs—all without the need for extensive coding.

All signs point to one crucial insight: the next significant milestone in AI automation is not just about replacing jobs, but about automating the process of automation itself. While challenges remain, this evolving framework promises to be the culmination of AI’s potential to enhance productivity—an achievement that might finally live up to its hype.

*Stay tuned for ongoing developments in AI automation