AI Monitoring for LLMs and Enterprise Agents

AI monitoring is the practice of observing AI systems in production to detect quality, safety, reliability, cost, and governance issues. For LLMs and agents, monitoring must go beyond traditional infrastructure observability.

Latency, errors, and uptime are still important, but they do not tell the full story. An LLM can return a fast response that is unsupported. An agent can complete a request while using the wrong tool. A retrieval system can return stale context. A workflow can succeed technically while violating policy or creating hidden risk.

The most useful monitoring programs classify recurring patterns rather than treating every incident as unique. If a system repeatedly produces unsupported answers, ignores retrieved evidence, violates schema, or loops through tool calls, those patterns should become detectable failure modes with clear mitigation workflows.

FailureModes.ai helps teams monitor LLM and agent systems through a failure-mode lens. The goal is to identify what is breaking, how often it happens, which users or workflows are affected, how severe the pattern is, and what remediation should happen next.

FailureModes.ai turns LLM and agent monitoring into a failure-mode system: every incident maps to a labeled, severity-scored pattern with a clear detection signal and mitigation path.