Experts handling complex service cases often face time pressure and information overload. Without immediate access to relevant case history, prior resolutions, and contextual insights, decision-making can slow down, leading to inconsistencies and extended turnaround times.The Case Resolution Guidance Agent supports experts by analyzing live and historical case data to deliver contextually relevant recommendations. Drawing from structured and unstructured inputs—such as case records, diagnostics, customer interactions, and prior resolutions—it identifies applicable actions, similar cases, and procedural references. This enables experts to make informed decisions efficiently while maintaining accuracy and consistency.Using LLM capabilities, the agent interprets narrative case details, extracts insights from past communications, and highlights next-step guidance based on established practices. This approach enhances operational efficiency, shortens resolution cycles, and strengthens service quality—allowing employees to focus on high-value problem-solving while ensuring cases are handled with clarity and consistency.
Accuracy
TBD
Speed
TBD
Sample of data set required for Case Resolution Guidance Agent:
Case: 8675309 Technician: Sarah Jenkins, ID 784 Date: 2023-10-26 Customer: Quantum Dynamics Manufacturing Asset: Automated Assembly Robot, ZX-9000 (Serial: Z9K-4811-B) Location: Production Line 3, Bay 2
Subject: On-Site Diagnostic Follow-up for Intermittent Shutdowns
Completed a 4-hour on-site diagnostic session for the ongoing intermittent shutdown issue with the ZX-9000 unit. Production Manager John Doe reports shutdowns are occurring without a clear pattern, approximately 2-3 times per 8-hour shift, causing significant line delays.
Key Observations:
ERR_VREG_04
(Voltage Regulation Fluctuation) on controller board C-12. The voltage drops are brief (less than 2ms) and do not always correlate directly with a full system shutdown, but they are the only consistent anomaly recorded.Conclusion: Standard diagnostic procedures have failed to isolate the root cause. The intermittent voltage drops on board C-12 are suspicious but not conclusive based on standard procedure guides. Escalating back to the expert squad for further analysis. All logs have been uploaded to the case file.
Sample output delivered by the Case Resolution Guidance Agent:
Recommended Actions and Insights Report
Case ID: 8675309 Customer: Quantum Dynamics Manufacturing Asset: ZX-9000 (Serial: Z9K-4811-B) Priority: Critical Analyzed Input:
Case_8675309_Field_Technician_Update
1. Key Insight from New Data
The technician's report of intermittent
ERR_VREG_04
alerts on controller board C-12 is the critical new insight. While AlphaScan diagnostics show no direct hardware failure, this specific error pattern is a known precursor to systemic failure in historically similar cases.2. Historical Context Analysis
Analysis of the knowledge base, including 1,284 historical cases related to the ZX-9000 model, has identified a strong correlation.
Finding | Confidence | Supporting Cases |
---|---|---|
In 87% of cases where ERR_VREG_04 was present without other hardware flags, the primary Power Supply Unit (PSU) failed within 72 hours. |
High | #789123, #812345, #854321 |
Standard AlphaScan diagnostics are known to incorrectly pass PSUs that are experiencing early-stage capacitor degradation. | High | InnovateCorp Tech Bulletin #TB-21-4 |
Controller board C-12 is highly sensitive to input voltage instability, making it the first component to log related errors. | Medium | ZX-9000 Engineering Schematics |
This pattern strongly suggests the root cause is imminent Power Supply Unit failure, not an issue with the controller board itself.
The following actions are recommended to accelerate resolution and prevent further unplanned downtime for the customer.
Primary Recommendation (Confidence: 95%)
Secondary Recommendation (Confidence: 80%)
Communication Recommendation
Proactively detects, predicts, and prescribes actions for potential escalations and SLA breaches in case management.
Feeds comprehensive case data and feedback into knowledge bases and AI models to drive continuous process improvement.
Automatically ingests, analyzes, and extracts actionable insights from customer feedback across channels to inform service improvements.
Provides actionable, context-aware recommendations to experts, accelerating high-stakes decisions and complex case resolutions.
Automatically detects bottlenecks, compliance gaps, and emerging trends in real time by analyzing all case activity.
Monitors, validates, and flags real-time case quality and compliance issues, automating audits and reducing manual reviews.