Traditional telemetry tells you what your AI agents did. Px Agentic Ops tells you why they cost so much — and exactly where to fix it.
Transforming raw agent event logs into visual process flows to identify the "Rework Tax" and optimise execution logic.
As organisations deploy AI agents at scale, costs become unpredictable and difficult to control. The agent controls its own execution loop — and you only see the bill.
You can see totals. You cannot see why specific sequences waste tokens, or which execution patterns are the source of runaway costs.
Input, output, per-request averages are all visible — but which request patterns are burning the most?
You see which tools are called most — but not why they're being called in wasteful sequences.
Per-session averages and peak hours are logged — but the root cause of cost spikes stays hidden.
You have data — but no insight into behaviour patterns or waste sources.
Px Agentic Ops transforms your agent event logs into visual process flow maps — with automatic pattern detection, rework loop identification, and token-cost attribution per execution path.
Every tool call sequence visualised as a process graph — frequency, cost, and variants all visible at a glance.
Automatically surface the "Rework Tax" — cycles where agents repeat tool calls unnecessarily, burning tokens.
Every execution path is annotated with token usage and cost — so you know exactly which sequences to fix first.
Here's how Px Agentic Ops works in practice — using an online store agent as an example to illustrate the discovery and optimisation process.
One pattern. One targeted fix. Compounding savings across every daily session.
Book a demo and we'll run Px Agentic Ops on a sample of your agent traces — and show you the patterns you're currently blind to.