Your agents are running. Do you know what they're doing?
Billing measures tokens, not outcomes. The answer to what your agents are actually doing is already in your telemetry.
Most enterprises now have agents in production. Scheduled ones that triage tickets, review pull requests, label issues, draft reports. Usually the only two signals anyone watches are whether the job ran and what the monthly bill looks like.
A harder question to answer: which of those agents is actually earning its keep?
The agent that never did its job
Last week when we activated execution telemetry for a client's agent fleet, one scheduled agent stood out. It had been running on schedule for days with nothing to suggest a problem.
The traces told a different story. Every run, the agent loaded its context, pulled the issue list, and reasoned through it. Then it failed at the one step that mattered. It wrote a summary of what it would have done and exited. Green checkmark. Full bill. Zero labels, ever.
That added up to a steady monthly bill for an agent whose only output was a report nobody read. And it was invisible from the billing dashboard, because billing measures tokens, not outcomes.
What we do
We help teams answer a different question than "did the agent run": whether it was worth running, and what to change if it wasn't.
Profile your existing agent harness. We instrument what you already have (custom agent harnesses, agent workflows, agent fleets) and reconcile execution traces against your actual AI usage and cost. In our client work the two agree within about 3%, which means every dollar can be traced to an agent, a run, and a step.
Cut cost without cutting intelligence. The expensive agent is rarely the dumb one; it's the one with avoidable habits. The same fleet above was re-listing an entire project board four times per run, injecting tens of thousands of tokens of stale memory, and crossing the 200k-token context boundary on the last few steps of every run, where per-token pricing jumps. None of that improved the output. Fixing it leaves the model, the prompt quality, and the results intact while removing the waste.
Make observability something that gets read. Telemetry nobody looks at is just storage cost. We build an agent that consumes your execution traces and billing data every day and tells you, in plain language: what ran, what it cost, what failed, what changed since yesterday, and what to look at. It also watches the audit trail itself (integrity, delivery health, unexpected sources) so the record you'd rely on in a review is one you can trust.
Why this matters now
Agent spend is becoming a real budget line, and the agents are increasingly autonomous. Boards and finance teams are starting to ask the question they ask of every other system: show me what it did and what it cost. "It ran on schedule" is no longer a sufficient answer.
The good news is that the answer is already in your telemetry. It just needs someone, or something, reading it.