This looks useful.

One pattern we've been seeing internally is that once teams standardize API interactions through a single interface (or agent layer), debugging becomes both easier and harder.

Easier because there's a central abstraction, harder because failures become more opaque.

In production incidents we often end up tracing through multiple abstraction layers before finding the real root cause.

Curious if you've built anything into the CLI to help with observability or tracing when something fails.

One thing I'm curious about here is the operational impact.

In production systems we often see Python services scaling horizontally because of the GIL limitations. If true parallelism becomes common, it might actually reduce the number of containers/services needed for some workloads.

But that also changes failure patterns — concurrency bugs, race conditions, and deadlocks might become more common in systems that were previously "protected" by the GIL.

It will be interesting to see whether observability and incident tooling evolves alongside this shift.

Interesting direction.

One thing we've been seeing with production AI agents is that the real risk isn't just filesystem access, but the chain of actions agents can take once they have tool access.

Even a simple log-reading capability can escalate if the agent starts triggering automated workflows or calling internal APIs.

We've been experimenting with incident-aware agents that detect abnormal behavior and automatically generate incident reports with suggested fixes.

Curious if you're thinking about integrating behavioral monitoring or anomaly detection on top of the sandbox layer.