The thing is, we’ve been retrofitting software made for humans for machines, which creates unnecessary complications. It’s not about model capability, which is already there for most processes I have tested, it’s because systems designed for people are confusing to AI, do not fit their mental model, and making the proposition of relying on agents operating them a pipe dream from a reliability or success-rate perspective.
This led me to a realization: as agentic AI improves, companies need to be fully AI-native or lose to their more innovative competitors. Their edge will be granting AI agents access to their systems, or rather, leveraging systems that make life easy for their agents. So, focusing on greenfield SaaS projects/companies, I've been spending the last few weeks crafting building blocks for small to medium-sized businesses who want to be AI-native from the get-go. What began as an API-friendly ERP evolved into something much bigger, for example, cursor-like capabilities over multiple types of data (think semantic search on your codebase, but for any business data), or custom deep-search into the documentation of a product to answer a user question.
Now, an early version is powering my products, slashing implementation time by over 90%. I can launch a new product in hours supported by several internal agents, and my next focus is to possibly ship the first user-facing batch of agents this month to support these SaaS operations. A bit early to share something more concrete, but I hope by the next HN thread I will!
Happy to jam about these topics and the future of the agentic-driven economy, so feel free to hit me up!