The most effective users of AI tools don't treat them as black boxes. They succeed by using AI to go deeper, understand the process, question outputs, and iterate. In contrast, those who get stuck use AI to distance themselves from the work, avoiding the need to learn or challenge the results.

AI is not a 'set and forget' solution. An agent's effectiveness directly correlates with the amount of time humans invest in training, iteration, and providing fresh context. Performance will ebb and flow with human oversight, with the best results coming from consistent, hands-on management.

Frame AI agent development like training an intern. Initially, they need clear instructions, access to tools, and your specific systems. They won't be perfect at first, but with iterative feedback and training ('progress over perfection'), they can evolve to handle complex tasks autonomously.

Building an AI application is becoming trivial and fast ("under 10 minutes"). The true differentiator and the most difficult part is embedding deep domain knowledge into the prompts. The AI needs to be taught *what* to look for, which requires human expertise in that specific field.

The process of guiding an AI agent to a successful outcome mirrors traditional management. The key skills are not just technical, but involve specifying clear goals, providing context, breaking down tasks, and giving constructive feedback. Effective AI users must think like effective managers.

While choosing a leading vendor is important, the ultimate success of an AI agent hinges on the deep, continuous training you invest. An average tool with excellent, hands-on training will outperform a top-tier tool with zero effort put into its refinement.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

Before any AI is built, deep workflow discovery is critical. This involves partnering with subject matter experts to map cross-functional processes, data flows, and user needs. AI currently cannot uncover these essential nuances on its own, making this human-centric step non-negotiable for success.

The most valuable AI systems are built by people with deep knowledge in a specific field (like pest control or law), not by engineers. This expertise is crucial for identifying the right problems and, more importantly, for creating effective evaluations to ensure the agent performs correctly.