To build a complex real-world business, the founding team did every job themselves. This hands-on experience provided critical insights that algorithms or data analysis alone could never uncover, such as knowing not to assign a driver if food isn't ready.

To build truly effective agents, adopt a "founder's level of service" mindset. This involves an intensive discovery process to become a temporary expert in the client's business, culture, and brand voice. This deep, meticulous care ensures the final AI system is perfectly aligned with the client's intentions.

The key to creating effective and reliable AI workflows is distinguishing between tasks AI excels at (mechanical, repetitive actions) and those it struggles with (judgment, nuanced decisions). Focus on automating the mechanical parts first to build a valuable and trustworthy product.

The quickest path to market is a pilot where you sell the desired outcome, not the software. Initially, perform the work manually with AI assistance behind the scenes. This validates customer value and pinpoints the most repeatable patterns to productize.

A practical framework for developing agentic AI is to first map the human workflow. Break down the task into discrete steps, identify which ones can be automated, ensure the necessary data is available, and then build the underlying tools and code blocks. Don't start with the technology; start with the human process.

Before writing code, manually perform the customer's workflow as a service. This unsexy approach ensures you deeply understand the process, enabling you to build a superior automated solution later. It's about fulfilling the task first, then building the software.

Instead of immediately building an AI agent, founders should first manually perform the target workflow as a service. This process allows them to deeply understand the pain points, map edge cases, and acquire initial clients. Only after mastering the job manually should they incrementally add vertical agents to automate specific steps.

AI agents require deep, nuanced understanding of specific workflows. YC's methodology, which forces founders to intensely engage with customers and iterate rapidly, provides the perfect training ground to acquire this necessary domain expertise, making it an ideal environment for this new class of startups.

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.