Agent Entrypoints

AI agents entering a repository cold waste large amounts of context reconstructing repository intent, topology, and authoritative workflows. Humans do this naturally through exploration. Agents pay for it directly in tokens, latency, and incorrect assumptions.

An agent entrypoint is a compact machine-oriented routing layer embedded at the top of a repository README.md. Its purpose is to minimize exploratory context spend and direct agents toward authoritative operational paths immediately.

The goal is not to teach the agent the repository. The goal is to route the agent toward the correct sources of truth as quickly as possible.

This distills into a single question:

“How do I compress the minimum viable understanding of a repository into the fewest tokens possible while preserving reliable navigation and correct usage?”

Some AI Development Lessons

I’ve got a tendency to take tools and frameworks in IT and immediately push them to their limits and beyond. Sadly, this often lands me into the trough of disillusionment quite quickly when exploring any new technology. On the flip side, it is through this process I often learn some great lessons. This article will cover lessons learned as it pertains to AI in an effort to help shortcut some of those that are starting to dive further into this incredible new world we are entering with AI.