Currently, there is a wave of founders bragging about how much code they can generate with LLMs. How many MVPs they can make. I did that too.

But as our vibe code slop vertical AI agent MVPs proceed to slop on early users, vibe debugging through traces creates a major human bottleneck. Eyeballs can only read around 600 tokens per minute. How are you going to add 9s of accuracy by reading 100k tokens of context from agent outputs?

Introducing: the agent-owned repo

It is similar to asking a strong engineer, “what usually happens when I run this code?” or “what kind of problems happen when you run this code?”.

When an agent spawns into Raysurfer's agent-managed repos, it is auto-populated with successfully run code from prior runs, with each file's code reputation and execution behavior linked in.

This is the next step after Raysurfer's previous launch of a private stack overflow for AI agents: extending the code reputation framework so agents can spawn back in with all of their prior scripts and logs, already knowing what works and what does not.

Scripts can be siloed per client, with shared scripts across an org. With code execution artifacts, understanding the AI's behavior is much clearer than reading tool JSON traces.

Agent-owned repos can still call your regular tools via HTTP, similar to Anthropic's programmatic tool-calling model.

The goal

Hopefully this helps long-running vertical AI agents become more consistent and deterministic by making the jump from serial tool calls to reusable code execution.