Troubleshoot workflow issues faster with qibb AI Copilot

It’s a situation every automation team knows: a workflow that has been running reliably suddenly stops working, or it still runs but the output is no longer what you expected.

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Now the investigation begins.

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You start reviewing nodes, checking configurations, validating API responses, and following execution paths to understand what changed. Was it a configuration issue? A failed API request? An unexpected data format? Or did the root cause occur much earlier in the workflow than the failure suggests?

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Finding the answer can take far longer than fixing the actual problem.

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As workflows become larger and more interconnected, troubleshooting becomes less about spotting obvious errors and more about narrowing down where to start looking.

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qibb AI Copilot helps make that investigation faster and more focused.

See it in action

In this demonstration, qibb AI Copilot analyzes a selected section of a workflow to identify the root cause of a failure. After sharing the relevant workflow context, it explains the issue, identifies where the problem originates, and generates a corrected version of the workflow that can be applied directly.

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Instead of manually inspecting every node and connection, users receive contextual guidance that helps them focus on the parts of the workflow most likely to be causing the issue.

Why troubleshooting gets harder as workflows grow

Modern workflows rarely consist of a handful of connected nodes.

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They often span multiple applications, APIs, data transformations, conditional logic, and business rules. When something goes wrong, the visible symptom is not always where the problem began.

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A failed API call might be caused by an invalid payload generated several steps earlier. A missing value might originate from an unexpected transformation. A seemingly minor configuration change can affect downstream processes that appear completely unrelated.

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The larger a workflow becomes, the more difficult it is to manually trace the chain of events that led to an issue.

A more guided way to investigate

qibb AI Copilot helps users investigate workflow issues through natural language interaction.

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Rather than manually reviewing every node, users can ask targeted questions about a workflow and receive guidance based on its structure and configuration.

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Depending on the situation, qibb AI Copilot can:

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• identify potential misconfigurations
• highlight missing or incorrect connections
• explain unexpected workflow behavior
• suggest where to focus further investigation
• recommend potential fixes

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The objective is not to replace technical expertise. It is to help users reach the most likely cause faster and with greater confidence.

From investigation to resolution

One of the biggest challenges during troubleshooting is deciding what to investigate first.

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qibb AI Copilot helps reduce that uncertainty by narrowing the scope of the investigation and providing context-aware explanations within the workflow itself.

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Instead of starting with dozens of nodes, users can focus on the components that are most likely contributing to the issue.

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In the demonstration above, qibb AI Copilot does not just explain why the workflow failed. It also generates a corrected version of the affected workflow, helping users move more quickly from diagnosis to resolution.

Real-world applications

Resolving workflow failures

When a workflow stops unexpectedly, qibb AI Copilot helps identify likely causes and guide users toward the affected part of the workflow.

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Investigating unexpected behavior

Not every issue results in a complete failure. qibb AI Copilot can help explain why a workflow is producing unexpected results and identify where further investigation should begin.

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Supporting business-critical operations

For workflows that support customer-facing or revenue-generating processes, reducing investigation time can help minimize operational disruption.

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Helping teams troubleshoot with confidence

Whether you are an experienced workflow developer or inheriting an unfamiliar automation, qibb AI Copilot provides contextual guidance that makes troubleshooting more structured and less dependent on trial and error.

Business impact

Troubleshooting is an inevitable part of operating workflow automation at scale.

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The difference lies in how quickly teams can move from identifying a problem to understanding its cause.

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By helping users investigate workflow issues more efficiently, qibb AI Copilot enables organizations to:

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• reduce troubleshooting time
• resolve workflow interruptions faster
• improve operational reliability
• reduce manual investigation effort
• increase confidence when managing complex workflows

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Instead of spending valuable time searching for the root cause, teams can focus on restoring workflows and keeping business processes running smoothly.

What’s next?

Troubleshooting is often only one part of the investigation.

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Sometimes understanding why a workflow behaved a certain way requires more than reviewing its structure. It requires visibility into what actually happened during execution.

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In the next feature spotlight, we’ll explore how qibb AI Copilot incorporates monitoring data and logs to provide deeper insight into workflow execution and help teams make faster, more informed decisions.

Learn more

Book a demo or reach out to your customer success manager to see how qibb AI Copilot analyzes workflow issues, identifies potential root causes, and helps users move from investigation to resolution faster.

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Attending IBC2026? Schedule a live demonstration with our team to explore how qibb AI Copilot can support your workflow automation goals.

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Because when workflows fail, knowing where to look is often half the battle.

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FAQ

^{What does qibb AI Copilot help with in this use case?}

^{It helps teams troubleshoot workflow issues faster by analyzing workflow context, identifying likely root causes, and guiding users toward the most relevant part of the workflow.}

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^{Why does troubleshooting become harder as workflows grow?}

^{Because larger workflows include more applications, data transformations, logic, and dependencies. The visible symptom is often not where the original issue began.}

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^{Can qibb AI Copilot do more than explain the problem?}

^{Yes. In this use case, it can also suggest fixes and generate a corrected version of the affected workflow.}

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^{Who benefits most from this capability?}

^{Teams managing complex workflows, inheriting unfamiliar automations, supporting business-critical processes, or trying to reduce investigation time when issues occur.}

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^{This article is part of the qibb AI Copilot Spotlight Series, where we explore practical ways qibb AI Copilot helps teams understand, troubleshoot, monitor, improve, and document workflow automation.}