How AI Can Identify Active and Struggling Call Center Agents Using Playwright

A call center can have hundreds of agents working at the same time.

Some are handling calls continuously. Some are waiting for the next interaction. Some are struggling with long conversations, repeated transfers, or unresolved customer issues.

For a supervisor, identifying these patterns across an entire contact center can be challenging.

A dashboard might show agent status, call duration, and the number of calls handled. But understanding which agents are performing normally and which ones may need help often requires looking at several indicators together.

At ClearTouch, a contact-center solutions provider, one interesting application of AI is helping identify active agents and agents who may be struggling, using operational data and browser automation to support the monitoring process.

The idea is not simply to watch agents more closely.

It is to help supervisors understand where attention is needed and make better decisions using the data already available in their contact-center systems.

Imagine a contact center with 150 agents working across multiple queues.

Finding these answers manually takes time.

Supervisors may need to navigate multiple dashboards, open individual agent records, review call histories, and compare performance metrics.

By the time they identify a potential issue, the opportunity to intervene may already have passed.

The challenge becomes even greater when the contact center operates across several teams, campaigns, or locations.

The goal is to turn scattered agent activity data into actionable insights.

Before using AI to identify active agents, it is important to define what “active” means.

An agent marked as active in a contact-center dashboard might be logged in but not handling a call. Another agent might be speaking to a customer, completing after-call work, or waiting for an interaction.

A useful monitoring system should distinguish between:

Available: The agent is ready to receive a new interaction.

On call: The agent is currently handling a customer interaction.

After-call work: The agent is completing notes or other tasks associated with the previous interaction.

Break or unavailable: The agent is temporarily not available for customer interactions.

Logged in but idle: The agent is signed in but has no active customer interaction.

These states should come from the contact-center platform's actual status data rather than being guessed by an AI model.

AI can then summarize the information and help supervisors understand patterns across agents.

For example, it could identify how many agents are currently on calls, how many are available, and how many have been idle longer than an agreed operational threshold.

That gives supervisors a clearer picture of current activity.

Identifying an active agent is relatively straightforward when reliable status data is available.

Identifying a struggling agent is more complicated.

An agent who handles fewer calls is not necessarily performing poorly. They might be handling more complex customer issues, working in a different queue, or dealing with a temporary system problem.

This is why AI should look for combinations of signals rather than judging an agent using a single metric.

An agent's average handling time is consistently higher than the relevant team benchmark, even after accounting for call type and complexity.

The agent frequently transfers customers to other departments, potentially indicating a knowledge gap or a routing problem.

The agent regularly spends more time than expected completing work after a customer interaction.

Customers repeatedly contact the organization about the same unresolved issue.

Call reviews identify recurring problems with process adherence, communication, or required disclosures.

An agent's recent performance differs significantly from their own historical pattern or an appropriate peer group.

Individually, these indicators may mean very little.

Together, they can help highlight agents or workflows that deserve further investigation.

For example, AI might flag an agent with unusually long calls and repeated transfers. A supervisor could then review a sample of interactions to determine whether the agent needs training, the routing process is ineffective, or the underlying customer issues are unusually complex.

The purpose is to identify where support may be needed—not to automatically label someone a poor performer.

Playwright is a browser automation framework that can interact with web applications.

In a ClearTouch-style contact-center monitoring workflow, Playwright can help automate the process of accessing an authorized dashboard, navigating to agent activity screens, applying filters, and retrieving information displayed in the application.

For example, a browser automation workflow might:

Playwright handles the browser interactions. AI can help interpret the collected information, summarize patterns, or identify agents who meet predefined review criteria.

The exact implementation depends on the ClearTouch application, the available data interfaces, and the monitoring requirements.

Where a supported API exists, using that API for operational data is generally more reliable than scraping a dashboard. Playwright is particularly useful when the workflow must interact with the web interface itself.

Imagine the following fictional agent activity data:

These figures are illustrative examples, not actual ClearTouch customer data or universal performance benchmarks.

An AI-assisted monitoring system could identify Agents B and D for further review because several indicators are elevated.

But it should not immediately conclude that either agent is underperforming.

Perhaps both agents handle more complex customer cases. Perhaps they are working with a recently changed product. Or perhaps the application is routing calls incorrectly.

The AI's role is to highlight a pattern and explain why it deserves attention.

A supervisor then reviews the relevant context before deciding what action to take.

Once the system has gathered reliable information, AI can help turn raw metrics into a readable operational summary.

These figures are hypothetical, but they illustrate the idea.

Instead of requiring a supervisor to inspect every agent record, the system presents the most relevant patterns first.

A supervisor can then investigate the agents or queues that need attention.

This approach can also help distinguish between an individual performance issue and a broader operational problem.

If several agents in the same queue have elevated handling times, the cause might be a product issue, a complicated process, or a sudden change in customer demand—not individual performance.

The value of AI-assisted monitoring goes beyond saving time.

It also creates an opportunity to move from reactive supervision to more informed operational management.

Rather than waiting for a customer complaint or a poor weekly scorecard, supervisors can investigate unusual patterns as they emerge.

However, the system needs reliable data, sensible thresholds, and appropriate human oversight to deliver meaningful results.

There is a risk in designing any AI-based performance system.

If the only goal is to identify agents who appear to be struggling, the technology can become a surveillance tool rather than a support system.

A long call does not automatically mean poor performance.

A low call count does not automatically mean low productivity.

A high transfer rate does not automatically mean an agent lacks knowledge.

The best implementation uses AI to identify possible issues, explain the supporting evidence, and recommend what a supervisor should investigate next.

That might mean coaching an agent, improving a knowledge base, correcting an IVR flow, adjusting call routing, or fixing a product problem.

In some cases, the agent is not the problem at all.

Good AI monitoring should help identify the cause of a problem—not simply identify the person closest to it.

Contact centers already generate enormous amounts of operational data.

The challenge is turning that information into timely, useful decisions.

By combining reliable agent activity data, browser automation through Playwright, and AI-assisted analysis, teams can build workflows that make it easier to identify active agents, surface unusual performance patterns, and help supervisors prioritize their attention.

For a contact-center solutions provider such as ClearTouch, this represents an opportunity to connect operational visibility with more intelligent workforce support.

Playwright can automate browser-based steps. AI can help interpret patterns. Contact-center data provides the evidence. And supervisors provide the context and judgment needed to make fair decisions.

The objective is not to have AI decide which agents are good or bad.

It is to help contact centers answer three practical questions:

What evidence suggests they may need help?

What action could improve the outcome for the agent and the customer?

That is where AI-assisted contact-center monitoring can become genuinely useful.