The reasons behind the widespread disappointment with AI are quite understandable.

Understanding the Real Potential of AI in Automation

In today’s landscape, it’s easy to feel underwhelmed by the hype surrounding artificial intelligence. Much of the current discourse is dominated by entrepreneurs and tech enthusiasts promoting superficial solutions—often marketing misfits dressed up in shiny packaging—aimed at tackling problems that may not even exist. This environment often feels like a chaotic, profit-driven noise, overshadowing meaningful advancements with spammy AI gimmicks.

However, beneath this clutter, there are quieter, more substantial efforts happening behind the scenes. Skilled programmers and developers are leveraging AI to craft custom automation workflows tailored to their specific needs. Unlike one-size-fits-all solutions, these automations tend to be highly adaptable and don’t necessarily require complete overhauls to implement across different use cases.

The true breakthrough in AI isn’t about eliminating individual jobs or automating singular tasks; it’s about automating the very process of automation itself. Typically, automating a task involves a sequence of 1 to 5 steps, sometimes involving loops, memory systems, and interactions with multiple APIs. While this may sound straightforward, orchestrating these steps effectively requires careful structuring.

Each step demands a well-crafted prompt, precise ordering, and seamless integration of memory to maintain context across interactions. Moreover, connecting to APIs for executing specific actions involves coordinating multiple ‘agents’—specialized AI components responsible for different functions. You need an agent that generates prompts, another that designs the architecture—including memory management—and yet another to interface with external APIs.

Fortunately, the tools for building such complex systems are already available. AI models have been generating their own prompts for some time, as evidenced by research like the 2023 paper “XXXX”. More recently, protocols like the MCP (Multi-Chain Protocol) embed instructions directly within their API interactions, streamlining the process.

Adding to this, platforms like AgentForge now support YAML-defined architectures, enabling AI systems to independently construct entire automation workflows—from sequencing prompts to managing memory—without manual coding.

The future is approaching quickly. What we are witnessing today is the final frontier of automation, and once mastered, it could eliminate the need for further manual intervention in many tasks. While the journey isn’t trivial, the most complex problems are within reach, promising a new era where the automation of automation becomes commonplace.

Stay tuned and keep exploring—what’s coming next may be transformative.