SaaS’s biggest barrier to entry has always been the workflows wrapped around its data stores, not the storage itself. In an agent-driven world, that moat shifts to how platforms coordinate and govern AI agents across business processes. Companies that build orchestration layers—routing tasks, managing approvals, logging outcomes—will lock in customers. Behind the scenes, evals become the strategic IP. Robust evaluation suites break tasks into measurable dimensions—tone, accuracy, tool use—and feed that data back to improve reliability, governance, and customer trust. On the product side, there’s a “minimum viable unit of saleable software.” It’s the point where buying a packaged solution costs less—in time and money—than rebuilding it with an LLM. Below that threshold, teams tend to hack together scripts; above it, they’ll pay for a ready-made tool. Meanwhile, founders chase traction metrics that look good on slide decks but don’t reduce investor risk. Early interest might land meetings but fails during due diligence if usage isn’t sticky or revenue doesn’t scale. Operational fatigue shows up as “work whiplash,” where shifting priorities without clear communication burns out employees. Clear decision ownership, timely updates, and defined roles prevent churn. On the web development side, the emerging Lighthouse Agentic Browsing audit scores pages on machine-readiness—think stable DOMs, accessible trees, WebMCP adoption. And if you need to track your AI sessions, Recall logs Claude Code chats locally and summarizes them offline, saving tokens and protecting privacy. Finally, cybersecurity faces disruption as agents automate migrations, degrade exclusive data advantages, and speed up vendor comparisons, while top talent keeps undervaluing startup risk, ignoring that a strong team and backers cap the downside.