Understanding Why Many People Feel Disappointed with Artificial Intelligence

Rethinking AI: Beyond the Hype Towards Practical Automation

In today’s landscape, it’s no secret that public perception of artificial intelligence often falls flat. Much of the skepticism stems from a barrage of aggressive marketing by tech entrepreneurs promoting superficial solutions—often dubbed “plastic wrap” responses—that address non-existent problems or oversell capabilities. The tech space can feel like a chaotic, profit-driven arena flooded with gimmicks and empty promises, making genuine progress seem elusive.

However, beneath this chaotic surface, a quieter revolution is underway among developers and engineers. Many are leveraging AI to craft bespoke automation tools designed to streamline workflows and reduce manual effort. Unlike one-size-fits-all solutions, these custom implementations tend to be highly specific and adaptable, often requiring only minor adjustments when deployed in different contexts.

The true breakthrough isn’t about fully automating entire jobs—it’s about automating the process of automation itself. Achieving this involves orchestrating a series of interconnected tasks, typically composed of 1 to 5 steps, which may include loops, memory components, and interactions with external APIs. At first glance, this might sound straightforward: define a sequence, connect it to essential services, and let the AI handle the rest.

But the reality is more nuanced. Each step requires meticulously crafted prompts, carefully ordered to ensure logical flow, and structured memory integration to maintain context across interactions. To make the system work seamlessly, multiple AI agents are often employed: one to generate prompts, another to manage the architecture—including memory management—and a third to execute API calls and process data.

Encouragingly, much of this infrastructure already exists. AI systems have been capable of generating their own prompts for some time, as evidenced by research like the 2023 paper linked here. Additionally, advancements such as the MCP protocol now embed execution instructions directly within APIs, enabling more cohesive and flexible integrations.

Moreover, innovative tools like AgentForge now incorporate YAML-defined architectures that allow AI to autonomously design and manage entire automation workflows—from sequencing prompts to handling memory—without requiring users to write a single line of code.

All these developments point towards a future where truly intelligent automation is within reach. The final piece? Patience. The challenge is complex, but once perfected, this will be the last major hurdle in automating repetitive tasks. It’s an exciting time—stay tuned, because the era of true automated intelligence is approaching rapidly.