Anyone who has tried to move an AI agent from a local demo into a production environment knows the exact moment the excitement dies. It’s usually when you check your LLM provider's dashboard and realize a single agent task got stuck in an infinite cycle, consuming millions of tokens and racking up a massive bill in minutes.
The scenario is painfully common:
An agent is tasked with modifying a config file. It attempts to write to a protected directory. The operating system returns a permission denied error. The agent forms a hypothesis that it needs to install a dependency, runs a package manager command, and tries to write the file again. It fails. It tries to install the package again. It fails.
It enters a tight loop, executing the exact same logical steps repeatedly inside a black box, completely unaware that it is wasting resources, until it hits its hard timeout limit.
Debugging in the Dark
