Guide · Agent operations
Monitoring AI agents well
AI agents do more than answer questions. They write files, run tools, call services, and remain active while people focus elsewhere. Monitoring gives teams enough operational context to use that autonomy responsibly.
What good monitoring answers
A useful agent-monitoring system should help an operator answer:
- Which machines are connected right now?
- Which supported agents appear active?
- Is the reporting path healthy?
- What changed during the selected time window?
- Which evidence supports each activity claim?
- Where is the system partially or completely blind?
What to watch
| Signal | Why it matters |
|---|---|
| Machine health | A stale or offline observer changes what you can conclude. |
| Agent presence | Shows where supported agents appear to be running. |
| Activity history | Helps reconstruct changes without watching every session live. |
| Evidence coverage | Separates a supported observation from a partial one. |
| Consent state | Confirms that collection remains authorized. |
A simple daily review
- Check for stale or disconnected machines.
- Review limited or conflicting evidence before healthy activity.
- Look at current agent activity across the authorized fleet.
- Move into history only when you need context.
- Record uncertainty instead of guessing through it.
Surveillance with a boundary
Phigon is a surveillance layer for AI agents. That does not mean collecting everything.
Good monitoring is specific about purpose, starts with minimal data, stays visible to the machine owner, and stops when consent is revoked.
More telemetry cannot repair weak authorization. Consent and coverage are separate requirements, and both must be healthy.