If you are about to run an AI agent proof of concept (POC), or you are already a few weeks into one, the hardest part is knowing what to look for. The AI agent POC metrics worth tracking for a customer support agent are resolution rate by case type, QA pass rate, CSAT, handoff quality, and audit coverage. Agree how each one is defined before the POC starts, because a number defined afterwards is hard to defend in front of your approval committee.
Many proofs of concept never reach production. Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027, pointing to escalating costs, unclear business value, and inadequate risk controls as the causes.
This guide covers the scorecard that holds up in a regulated operation like financial services, what good looks like week by week, the numbers that actually indicate if an agent will be successful at scale, and how to turn the whole thing into a business case your approval committee accepts.
What an AI agent POC should prove
A demo shows you the happy path, and a happy-path demo is one conversation chosen by the vendor. A proof of concept for AI customer service asks a harder question: does the agent handle your volume, on your systems, against your edge cases, under your controls?
Three questions sit underneath that, and your AI agent POC success criteria should map to them before a single ticket is routed:
Can it resolve the work end to end? Not acknowledge it, not route it, not summarise it for a human. Resolve it, including the back-office steps underneath the ticket.
Can you trust it unsupervised on this case type? Every case type has a different risk profile. A balance query and a subscription cancellation dispute do not need the same level of oversight.
What does it change commercially? Cost per resolved case, hours returned to the team, and the queue your team stops working through by hand.
Agree on the answers in writing before the POC starts, with a named owner for each. Teams that skip this step spend the final week arguing about definitions instead of results, and the committee reads an argument rather than a recommendation. The full sequence, from stakeholder sign-off through to the go-live decision, sits in our guide on evaluating AI agents in financial services.
The AI agent POC metrics that prove it is working
Five key metrics will help you get the full picture of the vendor you’re testing. Define each one in the scoping document, and insist the vendor reports on it the way you defined it.
Metric | What it tells you | How to define it so it holds up |
|---|---|---|
Resolution rate by case type | Whether the agent finishes the work | The customer's issue is closed with no human touching it afterwards. Reported per case type. |
QA pass rate | Whether the work was done correctly | The share of reviewed conversations that meet your existing QA rubric, scored by your QA team. |
CSAT by case type | Whether customers were happy with the experience | Your current survey, unchanged, with the response rate reported alongside it. |
Handoff quality | Whether failure is safe | How often the agent hands off, how fast, and whether the human receives full context. |
SLA and audit coverage | Whether back-office work is faster and traceable | Turnaround time per case against your current baseline, plus the share of cases with a complete audit trail. |
Two of these deserve more attention than they usually get.
Handoff quality is the metric buyers under-track. In our own buyer conversations across 2026, the handoff to a human came up more often than vulnerable-customer handling. It matters because a high resolution rate with poor handoffs simply moves your problem rather than solving it: the cases that reach your team arrive stripped of context, and your handling time per case goes up even as volume comes down. Measure the handoff, not only the resolution.
Scope the POC around one type of work. A POC that mixes customer-facing chat with disputes adjudication or KYC review produces a scorecard that’s difficult to evaluate, because the two halves share no metric: CSAT means nothing on a case the customer never sees, like KYC review, and turnaround time means nothing on a frontline conversation that closed in ninety seconds. Pick the shape that matches the work you want automated first, report its numbers on their own, and run the second shape as its own POC rather than a second column in the same spreadsheet.
What good looks like week by week in an AI agent POC
Split the POC into two windows and score only the second.
Weeks one to three: configuration. The agent learns your procedures, your tone, and your systems. Numbers move a lot and none of them count.
Week three onwards: the scoring window. The agent stops changing, live volume runs for up to 45 days, and these are the numbers that reach the committee.
This is the shape that Gradient Labs uses in its own deployments, and it is worth asking any vendor to commit to it in writing. The reality is that you can’t tell how effective an AI agent will be until you’ve moved past the configuration window.
On the numbers themselves, a well-configured agent in financial services resolves roughly 50–60% of in-scope cases on day one and reaches 80–90% in a mature deployment. Many deployments stall at 60–65% and never move again, so treat the day-one figure as a starting position rather than a ceiling, and ask the vendor what the improvement cycle after the POC actually involves. How to compare those figures across vendors fairly is covered in our AI resolution rate benchmark guide.
Yonder scoped its disputes work on turnaround time rather than resolution rate, which is the right call for cases the customer never sees. Disputes that previously took most of a week to decide now close in a day, and 80% are resolved without a second touch.
"Gradient Labs' frontline and back office agents talk to each other and keep the full context of a case. If evidence is missing, the customer hears about it in the moment... Cases that took us the best part of a week to decide now take a day. Better for our customers, and better for our team."
Antony Atkins, Senior Escalations Manager, Yonder
The AI agent POC metrics that muddy results
Five figures look like strong proof points but can be misleading. Check for each one before the scorecard goes anywhere near a committee.
A blended resolution rate. One number averaged across every case type hides the distribution. An agent resolving 90% of password resets and 20% of complaints can report 55% and tell you nothing about whether it handles the work you actually care about.
Deflection counted as resolution. A ticket that never reaches a human is not necessarily a problem that got solved. The customer may have given up, tried another channel, or called instead. Ask for the resolution definition in writing, and read our breakdown of deflection versus resolution before accepting a containment figure.
Agent CSAT compared against your human CSAT. Once the agent takes the routine volume, your team holds only the hard residue, so their score falls for reasons that have nothing to do with the agent. Compare the agent against the same case types as your team handled them before the POC began.
A sample too small to carry the claim. A 90% resolution rate across 40 conversations is noise. Agree a minimum volume per case type up front, and size it against how often your riskiest case types actually occur.
Case types chosen by the vendor. If the vendor picked the scope, the scope flatters the vendor. Choose the case types yourself, and include at least one that your own team finds difficult.
How to turn POC numbers into a business case
The POC does not end when the agent stops running. It ends when someone makes the final case to the approval committee, and in a regulated firm that case has to clear more than one body: an AI or model risk forum, a new product approval process, procurement, and often all three.
Four things make the case easier to approve.
Cost per resolved case against your current baseline. Work out what the same volume costs you today, fully loaded, then put the POC figure next to it. This is the line finance reads first.
Capacity framed as redeployment. Express the saving in hours returned and headcount redeployed to work the queue never allowed, rather than as roles removed. Boards approve growth without added headcount far more readily than they approve redundancy.
The risk controls, in detail. Name the guardrails that ran on every reply, the handoff triggers, and the audit trail per case. Gradient Labs runs more than 20 financial-services guardrails on every turn, covering vulnerability, complaints, financial-difficulty detection, and out-of-bounds advice, with coverage for FCA Consumer Duty, CONC, FDCPA, Reg F, and the EU AI Act. We are SOC 2 Type II certified, and every agent action, data point referenced, and tool executed is logged.
What happens after the POC. A POC result is a starting position. Set out how the agent improves from there, who does that work, and on what cadence, because the gap between a 55% day-one rate and an 85% mature rate is delivery work, not model magic.
On commercials, Gradient Labs prices per resolution, with a deployment guarantee: once we have scoped a use case, we guarantee the deployment, and if we do not deliver what we said we would, you get your money back. That matters in a business case, because it moves the risk of the numbers not holding from your budget to ours.
See the numbers on your own cases
The fastest way to settle which metrics matter is to run the agent against your own case types and watch the scorecard fill in. Book a demo and we will scope a POC around the work your team finds hardest.
Elizabeth Shew leads Brand and Advocacy at Gradient Labs, where AI agents handle customer support and back-office work for banks, lenders, and fintechs. Before that, she led customer marketing at Mastercard and built Dynamic Yield's customer marketing programme from the ground up, a decade spent turning customer results into industry-shaping stories. She writes about how support and operations teams actually put AI and technology to work. Before tech, she was a professional dancer in NYC.