The gap between an agent demo and an agent in production is wide and well documented. A crew that nails a task on stage will, at scale, loop forever, blow a budget, call a tool with garbage arguments, or quietly produce a confident wrong answer. The encouraging part: these failures are predictable, and most of them aren't about the model being too dumb. They're operational.
The usual suspects#
- Runaway loops — an agent that never decides it's done, burning steps and tokens.
- Cost surprises — no ceiling on how much a single run can spend.
- Bad tool calls — malformed arguments that fail or, worse, half-succeed.
- Silent quality drift — output that looks fine and isn't, with no way to see where it went wrong.
Treat them as operations problems#
Each of those has an operational answer, and they're the ones we've built in. Budgets cap steps and tokens so a run stops instead of spiraling. Typed tools validate arguments before they're sent. Per-step traces turn silent drift into something you can read and pinpoint. Approval gates keep a human between the agent and anything irreversible.
None of this makes the model smarter. It makes the system around the model trustworthy — which, in production, is the part that was missing.