Overview · 4 minute read
The surveillance layer for AI agents
The world is filling with agents that write code, operate tools, call services, and work for longer periods without constant human input. That creates enormous leverage—and a simple new responsibility: agents need to be monitored.
Phigon gives authorized teams a live, evidence-aware view of AI agent activity across the machines they manage. It helps you see what is active, what looks healthy, what happened earlier, and where the available evidence is incomplete.
Get started → · See how Phigon works
Why Phigon exists
When an agent works for a few seconds, watching it directly is easy. When many agents work across many machines, direct observation stops scaling.
Teams still need practical answers:
- Which authorized machines are online?
- Which supported agents appear active?
- Is the observer healthy, stale, or disconnected?
- What evidence supports an activity claim?
- Where did coverage disappear or sources disagree?
Phigon brings those answers into one private workspace. It is AI agent surveillance in the responsible sense: transparent oversight of software agents, with authorization, consent, and uncertainty built into the system.
Phigon monitors AI systems—not people. It is not designed for covert employee monitoring, hidden collection, or surveillance without informed consent.
What you get
A live operational view
See connected machines, supported agent activity, reporting health, and important coverage limitations without checking every machine individually.
Evidence-aware history
Review earlier activity while keeping the supporting evidence state attached. Missing data does not silently become a confident claim.
Clear privacy boundaries
Start with metadata-first collection. Machine owners see the requested scope, approve it locally, and can revoke that approval.
One shared language
Engineering, security, and governance teams can review the same operational facts without pretending the system knows more than it does.
From consent to review
- Authorize — An operator begins pairing for a machine they are allowed to administer.
- Consent — The machine owner reviews the requested collection scope on their own machine.
- Observe — A minimal local observer collects only the signals allowed by local policy.
- Evaluate — Agent-native, OS-native, and gateway records remain independent while evidence is assessed.
- Review — The workspace presents activity, health, history, conflicts, and coverage gaps.
Evidence before certainty
| Workspace state | What it means | How to respond |
|---|---|---|
| Supported | Required evidence is present and available sources agree. | Use the activity as supported operational context. |
| Limited | Useful evidence exists, but expected coverage is incomplete. | Review with caution and restore coverage where possible. |
| Conflicting | Independent sources disagree. | Investigate the conflict; do not choose a convenient answer. |
| Missing | There is not enough evidence to attribute activity. | Leave attribution unresolved. |
Ready to begin?
Connect one machine you are authorized to supervise, start with the smallest useful collection scope, and make your first evidence-aware review.