Your AI agents inherited every blind spot your dashboards have. Here’s why.

Your agents inherited every blind spot your dashboards have

Why this matters now

For years, operations leaders have relied on process metrics that look useful but miss how work really gets done. The risk used to be operational waste. Now those same incomplete views are being used to design AI agents.

Average handle times mask workarounds. System logs miss the manual work between applications. Dashboards show outcomes but hide the steps people take to reach them.

Those blind spots were always expensive. With agents, they become urgent. If the source data misses the real work, the agent inherits the same flaw and scales it faster.

The problems hiding in plain sight

Two examples show the gap between what systems report and how work happens in practice.

One U.S. insurer's benefit quote calls looked healthy by average handle time. Task mining showed a different story. Twenty percent of calls over ten minutes ran long because associates skipped a pre-authorization screen and manually searched for the same information. That workaround cost the equivalent of 24 full-time employees a year.

A global finance organization mined its onboarding process and found more than 25 copy and paste operations per call. Those steps added 250 seconds while customers waited. The automation business case came to $1.7M a year.

In both cases, system logs showed nothing wrong. The process was technically completing. The failure was visibility. The systems showed work getting done, but they did not show how people had to complete it.

The same blind spots, now running faster

The risk changes when an agent starts making decisions from incomplete process data. A human workaround stays local. An agent-driven workaround spreads faster and affects more work.

What this looks like in practice

At the insurer, an agent routing benefit quote calls would have no reason to enforce the pre-authorization step unless someone had mined the process and proved it mattered.

System logs would show the case moving forward and averages would make the process look acceptable. The skipped step would stay hidden unless task mining exposed it.

Design the agent from logs alone and the agent learns the workaround. It does not fix the problem. It repeats it at higher volume.

The same investment, doing a new job

Process and task mining already proved their value in two practical ways.

• They find operational waste, as the insurer example showed. Redesigning the benefit quote task recovered 48,000 hours a year.
• They accelerate legacy modernization by feeding GenAI Blueprint real execution data instead of assumptions.

A third job is arriving now. Agents need the same mined process reality to make the right call inside real work.

The operating loop

This is how the arc closes.

Mined process reality shapes the workflow. The workflow grounds the agent. The same mining discipline then reads what the agent actually does.

Process context goes in. Execution visibility comes out. That loop makes agents more reliable at enterprise scale. It also explains why Pega now lets third-party agents run inside those workflows directly instead of re-reasoning the process at every step.

The Pega foundation

Pega Process and Task Mining provides the foundation.

• Process Mining reads system event logs and maps how processes actually run.
• Task Mining captures desktop activity and surfaces the workarounds system logs miss.
• Mined insights flow into Blueprint, which designs the workflows agents run on.
• Gartner named Pega a Leader in its 2026 Magic Quadrant for Process Intelligence Platforms.

Process visibility at scale was always worth solving. Now it separates agents that help from agents that scale your worst habits.

For a closer look, read how process mining and task mining work together.

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