The transformative power of AI agents is unlocked by professionals with deep domain knowledge who can craft highly specific, iterative prompts and integrate the agent into a valid workflow. The technology itself does not compensate for a lack of expertise or flawed underlying processes.

Instead of simply providing polished answers, AI workflows should be designed to foster learning. This involves using AI to challenge an employee's hypothesis, identify weaknesses without auto-correcting, and critique reasoning, turning the tool into a coach that supports independent thought.

The key AI skill is evolving from crafting individual prompts to "loop engineering." This means defining goals and feedback systems that enable an agent to generate, self-review, and autonomously refine its output to meet a specific objective, minimizing the need for constant human-in-the-loop intervention.

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.

Users often abandon AI when its first output is poor, akin to firing a new employee after their first attempt. Instead, train AI by providing clear, specific, behavior-based feedback repeatedly. It learns from reinforcement just like a human, but at a vastly accelerated rate.

Expect your AI agent's skills to fail initially. Treat each failure as a learning opportunity. Work with the agent to identify and fix the error, then instruct it to update the original skill file with the solution. This recursive process makes the skill more robust over time.

Building an AI agent is the starting point, not the finish line. The real, ongoing work lies in optimizing its performance and training it on new information. This creates an essential new human-in-the-loop role focused on continuous improvement.

Instead of complex prompts, interact with AI agents as you would a human employee. When the agent makes a mistake (like a broken link), provide simple, conversational feedback. The agent can then understand the error and self-correct its process for future tasks.

When an AI agent performs poorly, the most effective solution isn't clever prompt engineering. Braintrust's CEO's strategy is to "close the session" and rewrite the evaluation script from scratch. This forces clarity on the definition of success, which is often the root cause of the agent's failure.