AI Agent Monitoring is the discipline of continuously seeing what your AI agent says to real customers — and how those customers feel about it — so you catch drift, errors, and poor experiences while they're still small, instead of finding out from a complaint, a regulator, or a viral screenshot.
You already do this for your people. Human agents are coached, calibrated, and scored; every interaction can be measured against a standard you defined, and you act on what you find. Your AI agent is now a channel talking to those same customers — and for most organizations, it's the only channel they can't see. Forward-thinking teams have noticed that gap and closed it, because they've stopped treating containment as the goal.The containment trap
Containment rate — the share of conversations the agent handled without a human stepping in — is the number your AI vendor puts front and center, because it's the number that makes the deployment look successful.
It's also the number that tells you the least about quality. Containment says a human didn't get involved. It says nothing about whether the customer got the right answer, whether the tone was right, or whether they left satisfied. And because a single AI agent runs the same behavior thousands of times, one flawed pattern isn't one bad conversation — it's the same bad conversation, at scale, before anyone has looked.
Klarna is the clearest illustration. In early 2024 its AI assistant was handling 2.3 million conversations — two-thirds of all customer service chats, the work of an estimated 700 agents. On a containment dashboard, that's a triumph. Yet by May 2025 the company was rehiring people and walking the strategy back, with the CEO conceding that when cost becomes “a too predominant evaluation factor… what you end up having is lower quality”.
The bot was containing conversations beautifully. The quality inside them was what nobody had been watching.
The failure containment hides
A customer asks an AI agent if they can “speak to a human”. They're not transferred, so they simply give up and close the chat.
At the agent level, that conversation was never escalated, so it counts as contained. On the vendor's dashboard, it's a success. In reality, the customer left with their problem unsolved and their patience gone. Monitoring is what turns that ‘contained’ conversation back into what it actually was: an unresolved one. Multiply it across a quarter and you have a satisfaction problem your containment rate is actively hiding from you.
And when a live agent goes wrong in a more visible way, the business is usually the last to know. When DPD's chatbot went rogue in January 2024 — swearing at a customer and writing a poem describing itself as ‘useless’ and DPD as ‘the worst delivery firm in the world’ — the company didn't catch it through its own oversight. It became a story because a customer shared the exchange and it went viral. Without monitoring, your earliest warning system is an angry customer with a screenshot.
What Conversation Intelligence shows you
Instead of the containment number the vendor hands you — which is the vendor essentially marking its own homework — Conversation Intelligence gives you an evidence-based view of every conversation.
Custom insight topics
Signals configured to your business — human escalation ("I want to speak to a human"), suspected frustration ("you're useless, this is a waste of time"), unmatched intent ("sorry, I don't understand"), and unable to assist ("I can't help with that") — turn "the agent went off" into a measurable number you can trend over time.
Sentiment
How customers actually feel, conversation by conversation, so souring interactions surface even when the agent technically ‘contained’ them.
Promoters
xNPS shows whether your AI conversations are creating promoters or quietly manufacturing detractors — the difference a containment rate will never show you.
Repeat contacts
A conversation the customer had to come back and have again isn't a win, however ‘contained’ it looked. Repeats expose the resolutions that didn't stick.
An independent lens
Because evaluagent sits outside the system that built the agent, you get a completely unbiased view of its performance — you're not taking the tool's word for its own quality.
What it looks like in practice
Monitoring is a live view you run on a rhythm. In practice that means a dashboard where xNPS and resolution are filtered to conversations handled entirely by the AI agent — and compared against your organization's average, so you can see instantly whether the AI channel is pulling satisfaction up or dragging it down.
Your insight topics are plotted as trends, so you see direction, not just a snapshot. And when something moves, you can drill into the actual conversations behind it and read, in the customer's own words, what's driving the number — then set proactive alerts so the right person hears about a spike without having to remember to log in and look.
The discipline that makes this stick is a simple loop: review the trend on a set cadence, diagnose the conversations behind any movement, act on the specific thing the data points to — a routing rule, a prompt, a piece of content — and then measure the next period to confirm it moved in the right direction.
Dashboards don't change anything by themselves; but a regular review cadence can.
Who this helps
Conversation designers:
See exactly which intents frustrate customers or trigger escalations, so they know what to fix in the journey rather than guessing.
Operational and CX teams:
A live read on whether the AI channel is lifting or dragging satisfaction, sat right next to the human channel for a fair comparison.
QA teams:
Extend the discipline they already run for people to the bot, using the same standards and the same evidence.
Contact center leaders:
One honest view of AI-versus-human performance to take into planning, instead of a vendor metric and a hunch.
That's the difference between assuming your agent is performing well and knowing that it is. Monitoring turns a live AI agent from a black box you hope is behaving into a system you can watch, explain, and improve — the fastest route from launch to real, defensible confidence, and the shortest possible time to value.
