Applied AI Engineering

Autonomous agents that read mail, post in Teams and edit SharePoint are arriving in enterprise tenants faster than the controls around them. An agent is a new kind of non-human user that takes instructions from any text it reads, so it needs the same containment you would give an untrusted contractor with admin tools, designed in before it is switched on.

A multi-agent software development platform was spending about $107 a day on model calls before it had any product traffic, and nobody could say which agent was spending it. The fix was attribution first, then caching, routing and hard caps, each verified against a full day of real traffic rather than a projection.

A voice agent’s tools fail in ways a chat agent’s don’t: the caller hangs up mid-sentence, a setting is missing, or the phone network refuses a transfer while someone is listening. The lesson from building our own AI receptionist is that every tool needs a safe fallback that still captures the caller, and that telephony features need proof on live calls, not just passing unit tests.