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Agents Root Cause Accelerator Agent
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Root Cause Accelerator Agent

Synthesizes context, diagnostics, and historical data to deliver probable root causes, empowering rapid technical problem resolution.

About the Agent

Quickly pinpointing the root cause of technical service requests remains a major challenge—often requiring extensive manual cross-referencing of context, diagnostics, and past incidents. This time-consuming process can result in lengthy downtimes and diminished customer satisfaction.

The Root Cause Accelerator Agent harnesses internal case context, troubleshooting summaries, system logs, diagnostic reports, and industry forum insights to autonomously analyze and propose likely root causes for each issue. By leveraging both structured and unstructured data—including historical case records, prior communications, and vendor technical bulletins—it delivers evidence-based suggestions directly to technicians, shortening investigative cycles.

This agent accelerates technical workflow efficiency by surfacing high-confidence root cause hypotheses early in the process. The result: reduced manual effort, fewer delays in problem resolution, and more consistent, data-driven outcomes—enhancing overall productivity, service quality, and the customer experience.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Root Cause Accelerator Agent:

Synthesized Case Data: CS-2024-8675


Case ID: CS-2024-8675
Customer: Quantum Dynamics Inc.
Product: FusionBI Analytics Suite v4.2
Reported Priority: High




Issue Summary


Quantum Dynamics reports that their primary FusionBI dashboard is experiencing intermittent application crashes. The issue exclusively occurs when users attempt to generate complex, year-over-year reports that query large datasets spanning multiple terabytes. Simpler, daily operational reports function without issue. The problem began approximately one week ago, with no known preceding system changes.




Key System Log Snippets (Application Server)


Timestamp: 2023-10-27 14:32:01 UTC

Deliverable Example

Sample output delivered by the Root Cause Accelerator Agent:

Root Cause Analysis Report: Case #CS-2024-8675


This report provides a ranked list of probable root causes for the intermittent application crashes reported by Quantum Dynamics Inc. based on the synthesized case data provided.




Proposed Root Causes (Ranked by Probability)






Rank
Probable Root Cause
Probability
Evidence & Supporting Data






1
Insufficient JVM Heap Allocation
High
The java.lang.OutOfMemoryError: Java heap space is direct evidence. The diagnostic report confirms memory utilization spikes to 98% during large queries. This is consistent with historical Case #CS-2022-1234 and Vendor Bulletin VB-FUSIONBI-078, which resolved similar issues by increasing the JVM -Xmx parameter.




2
Inefficient Data Query Structure
Medium
The issue is explicitly tied to "complex, year-over-year reports." Poorly optimized queries can cause excessive memory consumption when processing large datasets. This pattern matches multiple threads on industry forums for large-scale BI tools, suggesting query optimization as a common resolution path.




3
Application Memory Leak
Low
While the symptoms align with a memory leak, the crash's immediate correlation with specific actions (large queries) rather than a gradual decline over time makes this less likely to be the primary cause. No direct evidence of a leak was found in the provided logs.








Confidence Score


    

  • Overall Confidence in Primary Root Cause: 95%
      

    • The combination of explicit OutOfMemoryError logs and performance diagnostics provides a very strong signal.
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Recommended Next Steps for Resolution


    

  1. Immediate Action: Increase the maximum Java heap size for the FusionBI application server. Consult product documentation (KBA-4510) for recommended settings based on the customer's data volume.
    2. 

  2. Secondary Analysis: Request the specific data queries from Quantum Dynamics that trigger the crash. Analyze them for potential performance optimizations (e.g., inefficient joins, lack of indexing).
    3. 

  3. Monitoring: After increasing heap size, monitor memory utilization graphs during the next large query execution to confirm the issue is resolved and does not reoccur.
    4.

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