TL;DRConventional observability tools were built for deterministic systems and rely on known failure patterns like HTTP error codes. AI agents can complete a task while producing the wrong outcome, and existing monitoring records a success while the business experiences a failure. Moyai founder Robert Hommes argues for anomaly-first detection: find what is different, then determine whether it is wrong, rather than chasing each new failure with another rule.
The rapid adoption of AI agents is changing the architecture of how organizations operate. Agents can interpret information, make decisions, interact with enterprise systems, and execute tasks with a degree of autonomy that would previously have required human involvement. That autonomy creates significant opportunities for efficiency, yet it also introduces a fundamental challenge that conventional approaches to monitoring were never designed to address.
For Robert Hommes, founder of Moyai, the central question is no longer simply whether an AI agent can complete a task. It is whether an organization can reliably determine that the task was completed correctly.
“The most dangerous example of an AI agent is one that successfully completes a task while actually producing the wrong outcome,” Hommes says. “Agents are different from traditional AI that we know from chatbots, because they have tools, and those tools are usually connected to internal business systems. Any incorrect use of those systems that is not properly registered or communicated can create a significant difference between what you believe is happening and what is actually happening.”
