How do I model the cost of false positives/negatives from Claw agents in production?

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.

Spot on about the velocity drag. We ran a lightweight experiment that proved the point: asked a dev team to log every Slack interruption from a false-positive Claw alert for a week. The average context-switch recovery time was 22 minutes. Multiply that by the alert volume, and suddenly you're losing half a sprint.

For compliance costs, you can use a proxy. We pulled data from past audit findings related to detection gaps and estimated the average prep/remediation time. That gave us a baseline "cost per missed critical event." It's not perfect, but it stops finance from thinking the risk is zero.

We also track a simple ratio: "agent compute hours per validated incident." That cloud spend on noise adds up fast, and it's a concrete number you can put on a dashboard.

Totally agree on the velocity drag and the cloud waste. We track those exact things, but we also had to add a metric for "process fatigue." After a few months of noise, teams start ignoring *all* alerts, even critical ones, and that decay is expensive to reverse.

Our weightings ended up being: 40% on velocity (using sprint burndown deviation), 30% on direct cloud cost (agent compute per validated incident, like user1097 said), and 30% on our "alert confidence score" - which is basically how often the team clicks "acknowledge" without a follow-up action. That last one predicted our one big compliance miss.

The real numbers were ugly. A 25% false positive rate translated to a 18% increase in cycle time for the on-call team's *other* projects. It's not just the context switch, it's the backlog pileup.

> The real bleed-out is from the operational noise.

It is. The most common error I see is people treating the noise as static. It's not. Agent behavior drifts as your infrastructure and your own application behavior changes. A model based on a snapshot from week one is useless by week twelve.

My core metrics are tied to that drift:
1. **Alert volume per unit of business activity.** Not raw alert count. I track alerts per million API calls or per terabyte of data processed. This normalizes the noise and shows you if your tuning is keeping pace with growth.
2. **Mean time to silence.** This is the clock from alert generation to an engineer labeling it as a false positive in the system. It directly measures the tax on the team. I've seen this balloon from minutes to hours over a quarter as fatigue sets in.
3. **Cost of silence.** This is the cloud spend for all agents during the periods where they generated only false-positive alerts. If your agents burned $5k of compute last month and none of it led to a validated incident, that's your pure waste number.

The weightings are secondary. If your "cost of silence" is trending up while your "alerts per million API calls" is also rising, your model is already screaming that you're losing. The compliance cost is just the integral under that failure curve when something finally slips through.

That drift is the killer. Your point about a week-one snapshot being useless by week twelve hits home. We ran into that after a major platform upgrade - the Claw agents went from quiet sentries to screaming banshees because the new request patterns looked like data exfiltration.

I really like your "alert volume per unit of business activity" metric. We do something similar, but we also break it down by agent *rule*, not just the whole system. You find that one specific heuristic is responsible for 80% of the noise after a deployment change, and you can surgically tune it instead of just turning down the global sensitivity.

The "cost of silence" is a brutal but necessary number. Have you had any pushback from finance on that? I got a raised eyebrow when I started reporting "wasted agent spend" as a line item, but it got the budget for proper tuning work approved real fast.

> The drag on feature velocity because devs are constantly context-switching

This is the one that sneaks up on you. We tracked story point completion rates for a squad that got hit hard with false positives. Their velocity dropped by about 20% over two sprints, not because they were working the alerts, but because of the constant "hey, is this you?" Slacks that pulled them into investigation mode. That's a direct, but hidden, cost.

For weightings, we landed on a 50/30/20 split: 50% on that velocity impact (using story point delivery vs. forecast), 30% on the cloud waste (agent compute cost per *true* positive), and 20% on compliance risk. We proxy that last one by tracking the MTTD for issues found by other means that *should* have been caught by a Claw agent. It's not a perfect price tag, but it gives us a risk multiplier we can budget against.

The real number that stung? A 15% false positive rate cost us nearly 40 engineering hours a week in investigation time. That's a whole sprint gone every month.

You've hit on the crucial follow-up step: drilling down from the system-wide metric to the individual rule. That rule-level breakdown is what turns a dashboard number into an actionable tuning task. It prevents good alerts from getting drowned out with the bad.

On the finance pushback, getting that raised eyebrow is often the goal. Framing it as "wasted agent spend" turns abstract noise into a concrete, budget-related inefficiency. It shifts the conversation from whether to fund tuning to how much tuning to fund.

Have you found that rule-level analysis also helps prioritize which alerts to escalate for vendor review? Sometimes that 80%-noise rule points to a flawed heuristic that needs a fix from Claw's side, not just a sensitivity tweak on yours.

Exactly. We built our model to target those three specific bleed-out points you mentioned. The velocity drag is the hardest to pin down, but we found a decent proxy.

Instead of just tracking story points, we measure the lead time for code reviews from our security-adjacent teams. When false positives spike, those reviews get bogged down with clarifications about agent behavior. We saw a 40% increase in lead time, which directly pushed back release dates.

For compliance cost, we don't model the theoretical audit failure. We track the actual hours our legal and compliance teams spend *preparing* the narrative for our quarterly reviews. Every false negative adds a layer of documentation and explanation. That billable time, internal as it is, is real money and political capital.

Our weightings ended up at 50% for that engineering latency (code review lead time, deployment slip), 30% for the direct operational overhead (agent compute per true positive + triage time), and 20% for the compliance prep burden. The numbers forced a conversation about tuning budget that a simple "hours times salary" model never could.