Quality shouldn't stop at the handoff

A blended contact center, where AI and human agents share the work, needs shared quality standards, visibility across handoffs, and evidence for where to improve automation.

When quality assurance (QA) is fragmented, one customer experience can receive several separate verdicts. Human QA, call monitoring, and transfer reporting sit in different tools, often owned by different teams. While each view answers a useful question, the boundaries between them hide the full customer experience.

Adding AI introduces another boundary.

Consider a customer calling to change a delivery address. The AI agent checks the order and transfers the call. The customer repeats the order details, explains the request again, and waits while a person works out what the AI already tried. The address is eventually changed.

The AI agent’s report may record an appropriate escalation. The human conversation may pass its quality review. Yet neither verdict accounts for the effort created at the handoff. It’s worse if the call drops during the transfer.

For the customer, it was one request, and quality assurance should measure it that way. That means evaluating AI and human conversations together, against the same customer promise. Combined, those evaluations give teams a clearer picture of what the customer went through and evidence for where automation should go next.

Fragmented KPIs create different versions of success

An AI team may focus on containment, latency, and successful tool calls. A human QA team may review handling time, accuracy, and adherence to a service procedure. Those measures serve different purposes. Problems arise when they become competing definitions of a good customer interaction.

A conversation can count toward containment and still leave the request unresolved. A transfer can lower containment while protecting the customer by bringing in someone authorized to handle an exception. A human agent can score well on procedure adherence while the procedure itself creates unnecessary work.

Neither containment nor a QA score can explain any of these on its own.

The way work is split between AI and human agents also affects any comparison of their scores. When AI agents handle routine requests and escalate the complex or unusual ones to people, the human queue becomes harder. A raw comparison can confuse the difficulty of the work with the quality of the service. Applying the same scorecard to AI agents and humans doesn’t remove that difference.

A fair comparison looks at similar requests and asks why each one was escalated. That keeps the customer’s experience visible across the AI and human stages of the request.

Start with the customer promise

Shared quality standards begin with what the business owes the customer: accurate information, appropriate action, compliance with applicable policy, and a clear next step. They should also capture avoidable effort, such as repeating information or navigating an unnecessary transfer.

These expectations apply whether an AI agent or a person handles the request. The evidence used to assess them can differ.

For an AI interaction, an investigation might check whether the agent hallucinated, whether the knowledge it retrieved was relevant, and what its tools returned. For a human-handled interaction, it might examine the service procedure, available information, and what remained unresolved after the transfer.

The standard can be shared while the diagnosis remains specific.

The evaluations themselves need review, too. Teams should check automated, LLM-based evaluations against human-reviewed examples from their own workflows, and revisit them when policies or customer needs change. Consistent scoring is useful only if the criteria make sense and the judgments are dependable.

Follow the handoff and flag what’s missing

Customers experience the handoff as part of the service, so it belongs in the quality review. The reason for transfer, the context passed forward, and the work the customer must repeat all deserve attention. A final resolution shouldn’t erase the friction that preceded it.

In the delivery-address example, the investigation could reveal that the AI agent lacked permission to make the change. It could also reveal that the receiving team never got the order details. Those findings point to different interventions: review the permitted action in one case, repair the transfer in the other.

End-to-end observability depends on connected evidence. If only the AI transcript is available, the analysis should say that the human leg of the call is missing. If the record ends with a promise to change an address, confirmation from the order system is needed to establish that the change happened. Spotting repeat contacts means linking records across a defined time window.

Missing evidence should remain visible. Otherwise, a unified dashboard can create the same false confidence as separate ones.

Use human interactions to improve the service system

Human-handled conversations can reveal what automation is missing. A person may resolve a recurring request using knowledge the AI agent doesn’t have, an action it can’t take, or a workaround for a broken process.

Each pattern creates a concrete question to investigate. Should the knowledge source change? Can an approved action be added? Does the underlying process need repair? Some cases will still require human judgment, and the quality standard should recognize a well-handled escalation as a good outcome.

The improvement loop starts with a recurring problem and its supporting conversations. The team reviews the cause, changes the relevant workflow or AI behavior, and tests the change before expanding it. Afterward, it checks whether comparable requests show better resolution and less customer effort, alongside any change in automation.

Teams can then expand automation where the evidence shows the customer can be served well. To make those decisions, they need a connected view of quality across AI interactions, human conversations, and the handoffs between them.

How we’re approaching this at Parloa

We built Parloa Lens to bring conversation observability and diagnostic evaluations to service operations. It helps teams investigate quality, customer experience, and compliance alongside operational measures for AI and human agents, including end-to-end conversation journey analysis.

The aim is to apply shared quality standards to human and AI interactions across an organization's existing service operation, then use the findings to improve workflows and identify opportunities for better automation.

Teams can start with a defined business question and a scoped review of conversation transcripts. That review establishes which records are available, which standards can be evaluated, and where the evidence is incomplete. The output is a reviewed set of findings and improvements to test, with observed failures separated from suspected causes. Lens also supports ongoing observability across external systems.

For AI agents, Parloa Relationship Score combines Lens’s diagnostic evaluations into a single score out of 100. It gives the greatest weight to conversation experience, including customer effort and resolution, while trust and compliance failures cap the result. So a contained call that left the customer repeating themselves won’t score well, and neither will a pleasant one that broke policy.

When AI and human conversations are evaluated together across the whole journey, teams can see what to automate, where people add value, and how to improve the handoff between them.