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  • 1.  Using AI to automate Genesys Cloud alarm Troubleshooting

    Posted 11 hours ago

    Hi Community,

    I'm working on an idea to use AI to assist with Genesys Cloud issue and alarm troubleshooting.

    The concept is to provide an alarm/error such as Proxy Timeout, Device Unreachable, Call Quality Issue, Call Disconnected, or Traffic/Behavior Anomaly and have AI identify:

    1. Possible root cause
    2. Relevant Genesys Cloud components
    3. Logs/metrics that should be checked
    4. Recommended troubleshooting steps
    5. Possible resolution
    6. When the issue should be escalated to Genesys Support

    I would like to understand whether AI Copilot or other Genesys Cloud AI capabilities can support this type of troubleshooting workflow.

    Has anyone implemented something similar? I would appreciate any architecture suggestions, best practices, or real-world examples.


    #AICopilot(Agent,Supervisor,Admin)

    ------------------------------
    Raju YadaV
    ------------------------------


  • 2.  RE: Using AI to automate Genesys Cloud alarm Troubleshooting

    Posted 11 hours ago
    Hello Community,
     
    This is a very promising and forward-thinking idea. Using AI to streamline troubleshooting is a natural evolution for contact center operations, and it's great that you're exploring it. From what I've seen, Genesys Cloud Copilot is indeed the primary solution designed to address exactly this kind of challenge.
     
    Genesys Cloud Copilot is a native, conversational AI assistant that is embedded across the platform and is specifically designed to help administrators, supervisors, and other users move from reactive problem-solving to proactive performance management. For your use case, Copilot can directly assist with several of the key tasks you've outlined. You can ask Copilot questions in natural language to identify and understand issues, such as "Why did Tech Support queue AHT increase between noon and 3 PM?" and the AI will analyze relevant data to correlate performance drops with recent changes, like a routing configuration update. Beyond just identifying problems, Copilot can also provide actionable insights by not only identifying the potential root cause but also highlighting its impact and recommending actions to restore performance. Furthermore, it can help you fix issues directly; for instance, you could ask it to "Assign 3 agents from the General Support queue with Technical Support skill to the Tech Support queue," and it can complete the task instantly with built-in validation. For users who are less experienced or encounter a novel issue, Copilot provides access to trusted how-to guidance from the Genesys Cloud Resource Center, which helps onboard team members and reduce their dependency on expert users. It is also worth noting that Genesys is introducing a Copilot usage analytics dashboard that will help administrators track how Copilot is being used, identify the most frequently used AI Agents and tools, and measure adoption across the organization.
     
    While Copilot is clearly the right direction, it is important to be aware of the current state of the platform. Some of its most powerful features are currently geared toward agent assistance and after-call work. For example, troubleshooting guides for Agent Copilot often focus on configurations for agent-facing features, such as why suggestions aren't appearing on an agent's panel, issues with transcriptions for voice, or how to improve the accuracy of AI-generated summaries. Its ability to analyze system alarms and provide root cause analysis for infrastructure-level issues like Proxy Timeout or Device Unreachable may be a more advanced, administrator-focused capability that is still evolving.
     
    Based on the use cases Genesys presents, your architecture could be shaped around a few key principles. The primary interface would be the user asking Copilot questions in plain language, making it accessible to a wide range of users. The success of this workflow depends on Copilot's ability to access and correlate data across various sources, including performance dashboards, configuration histories, and logs. A key strength of this solution is that it doesn't just provide insights; it can also execute actions in a secure and governed manner, reducing manual effort and the risk of errors. You would also want to ensure your Knowledge Bases are well-structured for optimal AI performance, as Copilot uses Retrieval-Augmented Generation to pull and synthesize information effectively.
     
    In summary, Genesys Cloud Copilot is the native tool built for the exact challenges you have described. It can help you analyze issues, find root causes, and even resolve them through guided, conversational interactions. While some capabilities are more developed for agent-facing use cases, the platform is clearly moving toward a unified AI framework that empowers all contact center roles to optimize performance and troubleshoot more effectively. I hope this provides a solid foundation for your project and gives you confidence that you are on the right track. Good luck!


    ------------------------------
    Camila Meneghini
    ------------------------------



  • 3.  RE: Using AI to automate Genesys Cloud alarm Troubleshooting

    Posted 10 hours ago

    Hi Raju,

    I think the concept is viable, but I would make an important distinction between operational troubleshooting and deep technical/telephony troubleshooting.

    Genesys Cloud Copilot is definitely moving in this direction. Unlike Agent Copilot, which is focused on assisting frontline agents during interactions, Genesys Cloud Copilot is designed for administrators, supervisors, and operations teams. Genesys already documents use cases where Copilot analyzes operational data, correlates performance degradation with configuration changes, and recommends or executes corrective actions.

    Genesys Cloud Copilot overview:
    https://help.genesys.cloud/articles/genesys-cloud-copilot-overview/

    Genesys Cloud Copilot use cases:
    https://help.genesys.cloud/usecases/genesys-cloud-copilot/

    However, for examples such as Proxy Timeout, Device Unreachable, Call Quality Issue, unexpected disconnects, or SIP/network failures, I would not treat Copilot today as a complete RCA engine by itself.

    Genesys also makes an important distinction between Genesys Cloud Copilot and Agent Copilot:
    https://help.genesys.cloud/faqs/how-is-genesys-cloud-copilot-different-from-genesys-agent-copilot/

    In my experience working with Genesys Cloud APIs, Architect Workflows, Data Actions and Triggers, I would approach this as an evidence collection + AI reasoning architecture.

    For example:

    Alarm / Conversation ID
    → Trigger or orchestration layer
    → Collect Genesys evidence
    → Normalize the diagnostic context
    → AI analysis
    → Root-cause hypothesis + evidence + recommended actions + escalation criteria

    For a voice issue, I would try to collect as much deterministic information as possible before asking the AI to reason about it: Analytics Conversation Details, participants and sessions, disconnect reasons, SIP metadata/traces, SIP Diagnostics/PCAP when available, and media quality information such as MOS, jitter, packet loss and latency.

    Genesys SIP Diagnostics:
    https://help.genesys.cloud/articles/sip-diagnostics/

    This distinction is important. For example, asking an LLM to interpret only "Call Disconnected" would usually not provide enough evidence to determine the actual cause. The conversation/session context and signaling behind that result are much more valuable.

    The same principle applies to call quality. Genesys already provides diagnostic information around MOS, packet loss, RTT and jitter. An AI layer could correlate those values with known troubleshooting procedures instead of simply guessing from the alarm name.

    Genesys Cloud WebRTC Diagnostics:
    https://help.genesys.cloud/articles/run-the-built-in-genesys-cloud-webrtc-diagnostics-app/

    Triggers and Architect Workflows could also be useful for the orchestration side. Genesys Process Automation Triggers can react to platform events and invoke workflows, which could then call APIs/Data Actions or an external service responsible for building the diagnostic context.

    Process Automation Triggers:
    https://help.genesys.cloud/312208/

    I would also use Genesys Cloud Copilot where it already adds value, especially for operational analysis, configuration guidance and change correlation. For deeper telephony RCA, though, I would currently see a custom service consuming Genesys APIs and diagnostic data as the more realistic architecture.

    The key design principle for me would be:

    AI should interpret the evidence, not invent the evidence.

    If the model says "carrier issue", for example, I would want it to explain why: SIP response, disconnect reason, affected trunk, pattern across conversations, timestamps, network metrics, etc.

    Then escalation to Genesys Support could become another governed rule: if the available evidence points to a Genesys-managed component, the issue affects multiple conversations/users, or the available APIs/diagnostics cannot explain the behavior, generate an escalation package containing conversation IDs, timestamps, region, trunk/site, SIP evidence, metrics and troubleshooting already performed.

    I haven't implemented the complete AI layer for this exact use case, but I have used these Genesys components individually for troubleshooting and automation, and I think combining them this way would be technically realistic.

    I hope this helps and gives you some useful ideas for the architecture.



    ------------------------------
    Raphael Poliesi
    ------------------------------



  • 4.  RE: Using AI to automate Genesys Cloud alarm Troubleshooting

    Posted 5 hours ago
    For Genesys Cloud, if possible build these as an AI-powered Observability & Incident Intelligence layer.
     
    High-Level Architecture
    Option 1: Event-Driven Integration (Recommended)
    Step 1: Collect Events from Genesys Cloud
    Use:
    Genesys Cloud Notification API
    Genesys Cloud EventBridge Integration
    Analytics APIs
    Conversation APIs
     
    Step 2: Enrich the Event
     
    When an alert arrives:
     
    Plain Text
     
    Step 3: AI Correlation
     
    Send structured data to Azure OpenAI.
     
    Option 2: Copilot-Style Operations Assistant
     
    Create a Teams bot.
     
    Option 3: ServiceNow Integration
     
    which is extremely valuable for AI root-cause detection.
     
    Best AI Use Cases for Genesys Cloud
    Network
    High latency
    Packet loss
    Jitter spikes
    Edge disconnects
    Telephony
    Proxy timeout
    SIP 408
    SIP 503
    Registration failures
    Contact Center
    Queue traffic anomalies
    Sudden abandonment spikes
    Unexpected routing behavior
    Agent outages
    Security
    Fraud spikes
    Toll fraud
    Abnormal outbound traffic
    Login anomalies
    If you're in Microsoft/Azure
     
    A strong enterprise implementation would be:
     
    This provides near real-time AI-driven detection, root-cause analysis, remediation recommendations, and conversational troubleshooting for Genesys Cloud operations teams.


    ------------------------------
    Raja Sridhar Nagella
    ------------------------------



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