The reasons behind AI’s growing disappointment among many individuals

Understanding the Current State and Future Potential of AI Automation

In recent discussions about artificial intelligence, there’s a widespread sense of skepticism and disappointment. Much of this stems from the influx of corporate interests pushing shallow solutions—often marketed as groundbreaking—without substantial value. The tech industry has become saturated with hastily assembled AI “solutions” that resemble slot machines rather than meaningful advancements, all driven by a relentless profit motive.

However, beneath this noise, a quieter revolution is taking place. Skilled developers and programmers are leveraging AI to create tailored automation tools that streamline their workflows and improve efficiency. Unlike one-size-fits-all systems, these custom applications are adaptable; they don’t require complete overhauls when needs evolve. The reality is, comprehensive, universal AI solutions that do everything seamlessly are still on the horizon.

The most significant breakthrough in AI isn’t about replacing entire jobs overnight. Instead, the focus is shifting towards automating the process of automation itself. Typically, automating a single task involves a series of steps—anywhere from one to five—that may include loops, dependent memory components, and interactions with external APIs. At first glance, this might seem straightforward, but the implementation is complex.

Each step in this chain demands careful crafting of prompts, proper sequencing, and structured memory management. Connecting these steps to various APIs further complicates the process, often requiring multiple specialized AI agents: one to generate prompts, another to architect the overall system (including memory integration), and yet another to handle API interactions.

The exciting news is that much of this capability already exists. AI systems have been independently developing their own prompts and workflows for some time. Recent developments, such as the 2023 paper linked here (https://arxiv.org/abs/2310.08101), highlight progress in this area. Moreover, protocols like MCP are now providing direct instructions to language models via APIs, making the orchestration of complex tasks more streamlined.

Tools like AgentForge have introduced features such as YAML-defined architectures, enabling AI to autonomously construct entire automation frameworks—from prompt sequencing to memory management—without writing a single line of code.

All signs point to an imminent future where automation becomes entirely self-sustaining. While developing these systems is challenging, the ultimate goal is a single, comprehensive automation layer. Once achieved, it could be the final step in automating routine tasks—in effect, automating automation itself. Patience and continued