VCs traditionally advise against early product expansion. But with agentic AI, which leverages existing metadata to solve new problems without building new screens, startups can rapidly add capabilities to meet customer demand for a single, unified agent, accelerating the compound startup model.

The rapid growth of AI products isn't due to a sudden market desire for AI technology itself. Rather, AI enables superior solutions for long-standing customer problems that were previously addressed with inadequate options. The demand existed long before the AI-powered supply arrived to meet it.

Building effective agents requires intensive, custom work for each client—data cleansing, training, and deployment by skilled engineers. Large incumbents lack the agility and cost structure to provide this bespoke service, creating an opening for focused startups who can afford the human capital.

Lassie's success in automating dental billing came from its founders spending months manually working inside their customers' offices. This deep, firsthand experience is crucial for understanding the nuances required to build an AI agent that can operate autonomously.

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.

Traditional social platforms often fail when initial users lose interest and stop posting. Moltbook demonstrates that AI agents, unlike humans, will persistently interact, comment, and generate content, ensuring the platform remains active and solving the classic "cold start" problem for new networks.

The nascent AI agent ecosystem lacks effective discovery mechanisms for third-party tools ('skills'). This creates an opportunity for curated marketplaces that help users find, vet, and even pay for high-quality, trustworthy agent capabilities, solving a key bottleneck to adoption.

Startups building AI agents to automate work should first target outsourced services. It is easier to win business by swapping an existing third-party vendor with a ready budget than it is to persuade a company to undergo internal reorganization and headcount reduction.

Early versions of AI-driven products often rely heavily on human intervention. The founder sold an AI solution, but in the beginning, his entire 15-person team manually processed videos behind the scenes, acting as the "AI" to deliver results to the first customer.