An AI agent can repeatedly call the wrong tool, spend money and still look healthy to a conventional uptime monitor. Guidance drawn from StackGen’s production experience recommends tracing the entire agent session and enforcing limits before execution, rather than waiting for a service alert.
StackGen records each model call, tool execution and sub-agent delegation as a nested span, with latency and token cost attached. That preserves the chain of decisions needed to diagnose a loop or a task the agent claimed to finish but skipped. An asynchronous exporter can drop telemetry during an outage instead of blocking the agent itself.
Preventive controls include hard iteration caps, per-tool call limits and checks that reject identical consecutive requests. Comparing a session’s cost with an agent’s rolling average can reveal slower failures such as bad model routing, invented tools or expanding context. The recommendation is to keep tool calls, governance decisions and memory operations in a searchable, append-only log after removing credentials and personal data.
The guidance separates detailed traces for debugging from bounded metrics for alerts. Putting dynamic session identifiers into metric labels can create enough time series to overwhelm monitoring systems. OpenTelemetry’s emerging generative-AI conventions can provide a common schema, while evaluation tools can turn failed traces into regression tests.