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Enterprise agent surveillance
Large AI-agent fleets need consistent monitoring across machines, teams, and environments. Phigon provides an authorized surveillance layer for those agents while keeping tenant, consent, privacy, and evidence boundaries visible.
Start with a narrow pilot
Choose a small group of machines with clear ownership and one measurable operational goal. A pilot should prove that the supervision model is useful before the fleet grows.
Good pilot goals include:
- Identifying stale agent infrastructure faster.
- Creating a shared activity view for platform operations.
- Improving evidence quality during incident review.
- Verifying consent and collection-scope workflows.
Define ownership
Every machine needs an accountable owner. Every workspace needs a clear operator group. Every collection category needs an approved purpose.
Document who can pair machines, review activity, change scope, revoke credentials, and request deletion.
Roll out in stages
- Pilot — Validate one use case and the consent experience.
- Observe — Measure connection health, evidence coverage, and operator value.
- Standardize — Establish scope, retention, access, and escalation rules.
- Expand — Add teams and machines in controlled groups.
- Review — Audit access, consent, coverage loss, and deletion regularly.
Enterprise checklist
- Machine owners and workspace operators are clearly identified.
- Tenant isolation matches the organization model.
- Authentication fails closed in production.
- Metadata-first scope is the rollout default.
- Local redaction cannot be silently weakened.
- Conflicting and missing evidence reaches the right reviewer.
- Revocation, credential rotation, retention, and deletion are tested.
Large fleet or specialized deployment?
Contact Phigon with your organization, approximate machine count, supported platforms, use case, and privacy requirements.
Enterprise scale does not remove the consent requirement. Phigon is for transparent surveillance of AI agents, not mass surveillance of people.