You have AI agents running right now that nobody owns.

They were built by people who've since moved on. They're connected to systems nobody has reviewed recently. And when one breaks, drifts, or creates a bad output, nobody knows who is responsible.

Something will break.
Nobody will know whose problem it is.

As AI systems multiply across your organization, the question is not whether something goes wrong. It is whether anyone will know what it touched, who owns it, and what to do next.

Orphaned agents everywhere

AI workflows keep running long after the person who built them moves on. No owner, no oversight, no clear way to shut them down without understanding what they do.

AI outputs without lineage

When an AI-generated quote, customer response, analysis, or decision gets challenged, can you trace it back to the model, the data, the workflow, and the person who authorized it?

Accountability diffused across dozens of tools

Agent built in one platform, data sourced from another, output consumed by a third system. When something goes wrong, every team can plausibly say it was not theirs.

Every AI system has an owner.
Every output has a trail.

Proxon creates the ownership registry for your AI workforce — not just technical monitoring, but organizational accountability.

01 — DISCOVER

Find every running system

Proxon surfaces every AI agent, workflow, and automation operating across your org — including the ones nobody is actively monitoring. Full inventory, continuously updated.

02 — ASSIGN

Give everything an owner

Every AI system gets an assigned owner, a documented purpose, and a clear chain of accountability. Orphaned agents are flagged before they become incidents.

03 — TRACK

Trace every output

Full lineage of AI-produced work — what model, what data, what version, what workflow, what person authorized it. Alerts when systems go unowned, drift from their purpose, or create unexpected dependencies.

An ownership registry for AI work

Treat agents like employees: every system has a role, owner, budget, performance history, and escalation path.

Purpose and scope

What the AI system is supposed to do, what data it can touch, what decisions it can influence, and where its authority ends.

Owner and budget

Who is accountable for the system, which team pays for it, when it should be reviewed, and who gets alerted when it changes.

Lineage and dependencies

What systems feed it, what outputs depend on it, and what downstream work may be affected when it breaks or drifts.

Know who owns every AI system before someone asks.

Find unowned agents, map lineage, and give every AI workflow a clear owner, purpose, budget, and escalation path.