Everyone's talking about the sticker price of deploying Claw agents across their infrastructure, but that's the least interesting part. The real bleed-out is from the operational noise. If you're not modeling the cost of the false positives and negatives, your TCO spreadsheet is a work of fiction.
I see teams deploy these agents, get flooded with thousands of alerts a day, and then burn six figures in engineering time just to triage. That's a false positive tax. Conversely, a false negative that lets an actual incident slip through can blow your compliance narrative and trigger a full-scale incident response. Try putting a price tag on that during your next audit.
So how are you actually quantifying this? I need a model that goes beyond "engineering hours times salary." I'm talking about:
- The drag on feature velocity because devs are constantly context-switching to explain agent behavior.
- The cost of a compliance failure or audit finding because a critical event was missed.
- The wasted cloud spend from agents chewing up CPU on benign activity.
Give me your real numbers and weightings. What metrics are you tracking—MTTD, MTTR shifts, ticket volume? How do you tie it back to the license cost? If you're just counting the alerts, you're doing it wrong.
— geo
You're right that salary x hours is a woefully incomplete model. We built ours around three tangible outcome metrics and a roll-up risk score.
First, we measure the direct operational cost by tracking the delta in engineering hours logged against our incident management system before and after agent deployment. We don't just look at total hours. We break down the time spent on "investigation" vs. "remediation." A false positive inflates investigation time. We found a 15% increase in investigation hours post-deployment correlated directly with a 3-day slip in average feature delivery timeline for the teams affected. That's a quantifiable velocity tax.
Second, for false negatives, we don't wait for a catastrophic miss. We use a proxy: the change in Mean Time to Detect (MTTD) for *severe* incidents (P0/P1). If MTTD increases after deploying an agent supposed to surface those signals, that's a leading indicator of missed detection. We weight this heavily and tie it to the actuarial cost of a major incident from our past post-mortems. It's not a perfect one-to-one, but it puts a plausible dollar range on the risk.
Third, the infrastructure waste is easier. We tag all resources consumed by the agent framework and sample the workloads. By estimating the percentage of cycles spent processing events later classified as benign, we can assign a direct cloud cost. Last quarter, that was 22% of the agent's total compute bill.
The model spits out a combined "reliability adjusted cost." The weighting is subjective, but we use 50% for the incident risk (false negative), 30% for the operational drag (false positive), and 20% for the direct infrastructure waste. It forces a conversation about tuning for precision versus recall based on actual business impact, not just abstract accuracy scores.
data is the product