Monitoring

Monitoring is how you tell, at a glance, whether Syntic Code is working for your organization. It answers the operational questions that come up daily: are developers able to reach the model, are their requests succeeding, and is anything slow or failing in a way that will generate support tickets. Good monitoring turns a surprise outage into an alert you saw coming.

What to watch

The signals worth tracking cluster into three groups. Availability tells you whether syntic can reach api.syntic.ai or your gateway at all, and it is the first thing to check when developers report that the agent has stopped responding. Success and error rates show whether requests are completing, and a rising rate of authentication or rate-limit errors usually points at a credential or quota problem rather than a model one. Latency captures how quickly Amara begins streaming a response, which shapes how responsive the tool feels even when nothing is technically broken.

Where the data comes from

If you route traffic through an LLM gateway or self-hosted endpoint, that proxy is your best vantage point, because every request passes through it and you can emit uniform metrics and logs from one place. Without a gateway, rely on the CLI’s own diagnostics and your Syntic AI account’s usage view. A quick per-machine health check confirms the basics in seconds:

syntic doctor

Feed gateway metrics into your existing observability stack so Syntic Code sits alongside the rest of your services rather than in a separate silo.

Turning signals into action

Set alert thresholds against a baseline you capture early, when usage is small and healthy. Alert on sustained error-rate increases, on latency crossing a level users will notice, and on availability dips. Route these alerts to whoever owns the tool, typically your platform team, and document a short runbook so the on-call responder knows whether a spike means a network issue, an expired credential, or a hit rate limit before they start digging.