Recently, I’ve been reflecting on my journey with AI. While I consider myself a deep user compared to the average person—frequently using AI for web coding and keeping up with the latest research—I’ve realized there is still a significant gap between being a “depth” user and a true AI professional.
One of the most striking differences lies in orchestration capability. True experts can coordinate hundreds or even thousands of AI agents simultaneously to solve complex problems. My current workflow is much more modest, typically involving one or a few agents per hour. While effective, it lacks the massive scale and automated task decomposition that defines the “pro” level of agentic workflows.
A major observation I’ve made is what I call the “Efficiency Paradox.” In the long run, AI undoubtedly increases productivity. However, in the short term, the process of building the necessary infrastructure—such as creating custom Model Context Protocol (MCP) tools and configuring specialized agents—can actually slow down immediate progress.
I’ve found myself spending significant time developing these tools to empower my agents, which sometimes conflicts with my immediate KPI-driven deliverables. This highlights a challenge in prioritization: the constant tug-of-war between investing in long-term AI leverage and fulfilling short-term delivery requirements.
Using AI effectively isn’t just about knowing how to prompt; it’s about understanding the trade-offs in tool building, the complexity of agent orchestration, and the discipline of prioritization. As I continue to refine my workflow, the goal is to bridge the gap between building tools and delivering results, eventually scaling my “agentic” capacity to new heights.