The AI Observability Stack Behind Every Build
"It's working" isn't a metric. We monitor every agent we deploy for accuracy, cost, and drift, and send a monthly report that shows exactly what it's doing and what it's worth, not just an invoice asking you to trust us.
signals we track on every agent: accuracy, cost, latency, satisfaction, drift
reporting cadence, so you see the trend, not just a snapshot
team that both built the agent and watches it, no handoff to a stranger
Why "it's working" isn't good enough for the business
Most AI vendors hand you a login and disappear. The agent goes live, someone tests it a few times, it looks fine, and that's the last anyone checks until a customer complains. "It's working" becomes a feeling instead of a fact, based on the absence of complaints rather than any actual measurement of what the agent is doing across hundreds of conversations nobody read.
That gap is where most of the value of an AI system quietly leaks away. An agent that's 95% accurate sounds fine until you realize which 5% it's getting wrong, if a pricing question or a refund policy is in that 5%, the cost of not knowing is a lot higher than the cost of finding out. Without real monitoring, you don't get to choose which mistakes matter. You just find out about them eventually, usually from someone unhappy.
We built our observability stack because we got tired of clients asking "is it actually working?" and not having a confident answer beyond "nobody's complained." Every agent we deploy reports back: what it handled, what it cost, what it got right, and what it got wrong, so the question has a real answer every month, not a shrug.
What we monitor
Five signals, checked on a set schedule, not whenever someone happens to remember to look.
Accuracy & Escalation Rate
A sample of real conversations reviewed against known-correct answers, plus how often the agent hands off to a human. A rising escalation rate is usually the first sign something in the agent's knowledge or logic has gone stale.
Cost per Conversation/Task
Token spend and API cost broken down per interaction, tracked over time, so a bloated prompt or an unnecessary extra tool call shows up as a specific, fixable number instead of a mystery line on next month's bill.
Latency & Uptime
How long the agent takes to respond, and whether it's reachable at all. A slow agent loses customers just as effectively as a wrong one, and an integration that silently drops keeps the agent talking while it's actually broken.
Satisfaction Signals
Where they exist, thumbs-up/down responses, repeat questions, conversations that end in frustration or abandonment, read as a signal, not just a number in a spreadsheet nobody opens.
Drift Over Time
This month's accuracy compared against launch-month accuracy, on the same test set. Drift is slow and quiet by nature; the only way to catch it early is to keep measuring the same thing the same way, every month.
Covered by our 100% refund guarantee
Monitoring is built into every deployment, not sold as a separate add-on after something's already gone wrong.
The monthly ROI report
What actually lands in your inbox, once a month, whether or not anything went wrong.
Volume and outcomes
How many conversations the agent handled, how many it resolved without a human, and how many it correctly escalated, so you can see what it's actually taking off your team's plate that month.
Cost and accuracy trend
Cost per conversation and accuracy score, plotted against last month, so a slow drift in either direction is visible before it becomes a problem you hear about from a customer instead of a report.
What we changed, and why
The specific fixes made that month, a prompt update, a new FAQ added, a broken integration patched, tied to the specific issue that prompted each one. Not a generic "all good," an actual list.
Catching failures before your customers do
Alerts and thresholds set before launch, not improvised after the first complaint.
Thresholds set with you, not for you
Before an agent goes live, we agree what counts as urgent for your business specifically: an outage always is, a wrong answer on a pricing question is, a slightly slower response on a low-stakes FAQ usually isn't. Generic thresholds miss the failures that actually matter to you.
A defined response, not a scramble
Every alert has an owner and a response time attached to it in advance, so when something does trip a threshold, there's already a plan instead of a group chat trying to figure out who's supposed to look at it.
What's included in the retainer
Monitoring is one part of a wider maintenance retainer that also covers bug fixes, prompt tuning, model updates, and priority support, all handled by the same team that built the agent in the first place. If you want the full breakdown of what's included, what isn't, and what the tiers cost, that's on our maintenance page.
See What's In a Real Retainer →Tools and stack we use
The observability layer sits alongside whatever platform the agent itself runs on, logging, evaluation, and alerting tooling appropriate to the size and risk profile of the deployment, from lightweight logging on a single WhatsApp bot to a full evaluation pipeline on a multi-agent system handling payments or medical intake. We pick the tooling to match what the agent is actually doing, not the other way around.
Questions about AI agent monitoring
Free Walkthrough
See a Sample Observability Report
We'll walk you through a real (redacted) monthly report so you can see exactly what monitoring actually produces before you decide whether it's worth paying for.
30-min call · No sales pressure