Why it’s no surprise that people feel disappointed with AI

Understanding the Current State of AI: Beyond the Hype

In recent discussions about artificial intelligence, it’s common to feel underwhelmed or even skeptical. Much of the public discourse tends to be dominated by entrepreneurs and tech enthusiasts pushing superficial solutions—often marketed with bold promises but lacking meaningful substance. This environment can make AI seem like a distraction—just another commodity chasing profits with flashy tools and quick fixes.

However, beneath the noise, a quieter revolution is unfolding. Skilled developers and researchers are leveraging AI’s true potential to develop bespoke automations that significantly simplify workflows and enhance efficiency. Unlike generic solutions, these tailored implementations often operate independently, requiring substantial overhaul to adapt from one environment to another. Real progress won’t come from a one-size-fits-all approach but from creating systems that address specific challenges with precision.

The key insight is that the most transformative breakthrough isn’t about automating entire jobs but about automating the very process of automation itself. Typically, a single task involves multiple steps—each potentially looping or interacting with different data sources—where some form of memory or state management is essential. Designing these multi-step processes demands careful structuring: crafting prompts, sequencing operations, managing contextual memory, and integrating with external APIs.

This complexity usually calls for multiple AI agents: one that generates prompts, another that constructs the overall architecture (including memory handling), and a third that interacts with APIs to perform specific functions. Fortunately, much of this capability already exists. Researchers have long experimented with AI systems that generate their own prompts and workflows. Noteworthy developments, such as the 2023 paper on advanced orchestration techniques (see https://arxiv.org/abs/2310.08101), demonstrate progress.

Recently, tools like the MCP protocol have further streamlined this process. MCP allows defining instructions directly within API calls, and recent integrations—such as YAML-based architecture configurations in platforms like AgentForge—enable AI to autonomously build complex, memory-aware workflows from scratch, all without manual coding.

What does this mean for the future? It suggests we’re approaching a point where automating the process of automation will become the final, most impactful challenge. Although complex, solving it will unlock new levels of AI-driven productivity and innovation. The era of simple chatbots and superficial AI applications is giving way to sophisticated, adaptable systems designed to tackle real-world problems efficiently.

Now, we wait for these advancements to mature, knowing that the foundation is already in place. The true revolution in AI will be the automation of automation itself—