It’s understandable why people feel disappointed with AI.

Understanding the Reality of AI: Beyond the Hype and Capitalist Noise

In recent times, public perception of artificial intelligence (AI) has often been underwhelming, and there’s a good reason for that. Much of the media and online discourse is dominated by loud voices—primarily well-funded tech entrepreneurs—trying to market AI solutions that often feel disconnected from real-world needs. Many of these pitches lean on sensationalism, promising futuristic transformations or quick fixes, but often delivering little substantive value. This landscape can seem overwhelming and, frankly, a bit cynical, filled with flashy tools designed more to attract investment than to solve genuine problems.

However, beneath this noisy surface, a quieter evolution is taking place. Skilled programmers and developers are leveraging AI in practical ways—building custom automations, streamlining workflows, and handling complex tasks that were once manual and time-consuming. Interestingly, these implementations tend to be highly specialized; solutions that work perfectly in one context rarely transfer seamlessly to another without significant modification. This indicates that a “one-size-fits-all” AI solution is still some distance away and may not be feasible for the foreseeable future.

But the true potential of AI isn’t in replacing entire jobs or automating every task simultaneously. Instead, the breakthrough lies in automating the process of automation itself. This involves creating systems capable of managing and executing chains of tasks—often between five or fewer steps—that may include loops, memory interactions, and API integrations. At first glance, this might sound straightforward: just connect some prompts, a bit of memory, and APIs, and you’re done. In reality, each step requires meticulous prompt design, ordered execution, and a well-structured memory system to inform the subsequent actions.

Building these multi-agent workflows isn’t trivial. It requires several specialized agents: one to generate prompts, another to construct the overall architecture—including memory management—and yet another to call APIs and process responses. Thankfully, recent advancements are bringing these components closer together. For example, research from 2023 introduced techniques where AI systems can generate their own prompts, effectively self-optimizing their workflows (see the paper here). Additionally, protocols like MCP (Multi-Chain Protocol) now facilitate the direct integration of instructions within APIs, streamlining communication between systems. The introduction of YAML-based architectures into platforms like AgentForge allows AI to autonomously build complex, multi-step processes from scratch, linking prompts, memory, and API calls without