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A slow order-history screen can make a call look like an agent-performance problem. In a September 29, 2026, DEV Community account, Serguey Shinder describes a team whose average call length reportedly rose by about 40 seconds after a system upgrade. The account says the screen delay was already known to IT, yet 12 agents were put on coaching plans before the slowdown was prioritized.
What happened in the account
Shinder says an October system upgrade was followed by longer customer-service calls the next winter. For customers with long order histories, the order-history screen reportedly took 8–15 seconds to open. A typical call opened that screen three or four times, leaving agents with “mostly silence while the agent waited.” Shinder reports that average call length increased by about 40 seconds.
Although IT reportedly knew about the response-time issue, customer service initially treated the longer calls as an agent-performance problem. According to Shinder, 12 agents were placed on coaching plans. Once the delay was prioritized, the fix took three weeks; the account says average call length returned to its previous level and the plans were withdrawn. These are details from one firsthand account, not independently verified findings or contact-center benchmarks.
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Call length and application response time describe different things. An agent can spend more time on a call because a customer needs more help, because the agent must do additional work, or because the software takes longer to respond. If a performance report shows only the overall duration, those causes can be difficult to distinguish.
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For one documented example of metric components, AWS defines average handle time using contact-record durations that include interaction, customer hold, after-contact work, and agent pause duration for tasks. Its Amazon Connect agent-performance dashboard describes handle-time breakdowns, benchmark comparisons, performance evaluations, and agent-level views. These documents illustrate ways to inspect a metric; they do not establish that Shinder’s organization used Amazon Connect or that those features would have identified its reported screen delay.
Translate system latency into the department’s workload
Shinder says the issue gained priority after response time was expressed in operational terms—such as seconds per call, orders per hour, and invoices per day—instead of being presented only as an IT measure. In the account’s example, a ten-second delay across about 40,000 monthly screen uses represented roughly 110 staff hours. The arithmetic is 10 seconds × 40,000 uses ÷ 3,600 seconds per hour, or about 111 hours. This is Shinder’s estimate for that example, not a general estimate for other teams.
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The practical point is to connect a technical delay to the work it interrupts. A response-time figure becomes easier for department leaders to assess when it is paired with how often the affected screen is used and which operational measure is changing. Shinder says leaders then saw the same figures in monthly reports, allowing team metrics to be considered alongside system performance before coaching decisions.
How to investigate a sudden change before coaching
The account suggests a useful diagnostic sequence: compare the timing of the metric change with system changes, then narrow the aggregate measure to the activity and people affected. This is a practical approach inferred from the account, not a universal causal rule.
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- Pinpoint when the metric shifted. Compare the start of the change with software releases or workflow changes. In Shinder’s account, the longer calls followed a system upgrade.
- Inspect the work behind the aggregate. Break call or handle time into interaction, hold, after-contact work, and other available components. Look for a pattern consistent with waiting during a particular task rather than assuming all added time reflects the same behavior.
- Measure the affected screen or task. Check response time for the relevant action, and identify how often it occurs in a typical call or work period. Compare affected cases with cases that do not use the slow screen.
- Compare team and individual patterns. If several agents show a similar change around the same time, investigate shared systems and processes as well as individual performance. If the increase is concentrated among particular agents or cases, examine those differences too.
- Put the operational cost beside the technical measure. Estimate the time consumed using observed delay and usage volume, label the assumptions, and share the result with both IT and department leaders.
- Reassess after a fix. Check whether the response-time measure and the affected call metric move as expected before deciding whether performance coaching is still appropriate.
What the account does—and does not—show
Shinder’s account makes a specific point: a performance number can include time spent waiting for a system, so a change in that number deserves a check for changes in the application or workflow before it is attributed to worker behavior. Shinder summarizes the mismatch this way: “When a system slows down, the cost does not usually arrive at IT.”
The account reports that connecting response time to department-level work helped the issue receive priority, followed by a fix and the withdrawal of the coaching plans. It does not independently establish the cause of every reported change, prove that the same pattern applies to other organizations, or provide a general estimate of the effect of software delays.
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Sources: Serguey Shinder’s DEV Community account, September 29, 2026; AWS Amazon Connect real-time metrics definitions and AWS agent performance dashboard documentation.
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