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BigPanda Updates Root Cause Change Feature with New GenAI Capabilities

BigPanda launched its updated Root Cause Change (RCC) feature with new GenAI capabilities to automate incident analysis – identifying root cause faster and with higher accuracy than humanly possible.

Organizations are grappling with a surge of incidents tied to frequent infrastructure/software changes, with 85% of issues originating from these shifts and traditional IT tools can't keep up. RCC will now leverage explainable AI/ML to correlate incident alerts with infrastructure change data and improve incident root cause identification, reducing response times by 50%. With BigPanda's updated RCC feature, you're able to:

■ Reveal change data linked to IT incidents in real-time: Correlate multi-source alerts with change data to identify the probable change that caused an incident.

■ Leverage multi-source change aggregation: Connect to all change feeds and tools (such as ServiceNow, JIRA, Jenkins and CloudTrail), and aggregate their data.

■ Utilize real-time IT Incident correlation: Correlates multi-source alerts with change data to identify incident-impacting changes across hybrid cloud environments.

■ Apply pragmatic, explainable AI: BigPanda's pragmatic AI provides explanations why suspected changes were linked with an incident in easy-to-understand language.

■ Collaborate with change tools and teams: If a potential change is matched, users can explain their choice and propose next steps, which will be communicated directly to the tool that initiated the modification.

■ Report analytics: A new integration with Unified Analytics enhances efficiency in measuring, improving, and operationalizing root cause change investigation across all applications and services.

Customers share their POV on working with BigPanda:

"With BigPanda, our IT noise is not only reduced, but we are able to identify root cause in real-time- who the responsible team is, who owns the service that's alerting, etc. which is significantly reducing our MTTR. One of the biggest drivers that we have right now is auto-remediation."
Priscilliano Flores, Staff Software Systems Engineer at Sony Interactive Entertainment

"Previously, we had upwards of 2,500 alerts in a day. With BigPanda we achieved a 94% reduction in alert noise, which allowed us to consolidate those 2,500 alerts into 150 actionable events."
Sanjay Chandra, CIO at TiVo

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BigPanda Updates Root Cause Change Feature with New GenAI Capabilities

BigPanda launched its updated Root Cause Change (RCC) feature with new GenAI capabilities to automate incident analysis – identifying root cause faster and with higher accuracy than humanly possible.

Organizations are grappling with a surge of incidents tied to frequent infrastructure/software changes, with 85% of issues originating from these shifts and traditional IT tools can't keep up. RCC will now leverage explainable AI/ML to correlate incident alerts with infrastructure change data and improve incident root cause identification, reducing response times by 50%. With BigPanda's updated RCC feature, you're able to:

■ Reveal change data linked to IT incidents in real-time: Correlate multi-source alerts with change data to identify the probable change that caused an incident.

■ Leverage multi-source change aggregation: Connect to all change feeds and tools (such as ServiceNow, JIRA, Jenkins and CloudTrail), and aggregate their data.

■ Utilize real-time IT Incident correlation: Correlates multi-source alerts with change data to identify incident-impacting changes across hybrid cloud environments.

■ Apply pragmatic, explainable AI: BigPanda's pragmatic AI provides explanations why suspected changes were linked with an incident in easy-to-understand language.

■ Collaborate with change tools and teams: If a potential change is matched, users can explain their choice and propose next steps, which will be communicated directly to the tool that initiated the modification.

■ Report analytics: A new integration with Unified Analytics enhances efficiency in measuring, improving, and operationalizing root cause change investigation across all applications and services.

Customers share their POV on working with BigPanda:

"With BigPanda, our IT noise is not only reduced, but we are able to identify root cause in real-time- who the responsible team is, who owns the service that's alerting, etc. which is significantly reducing our MTTR. One of the biggest drivers that we have right now is auto-remediation."
Priscilliano Flores, Staff Software Systems Engineer at Sony Interactive Entertainment

"Previously, we had upwards of 2,500 alerts in a day. With BigPanda we achieved a 94% reduction in alert noise, which allowed us to consolidate those 2,500 alerts into 150 actionable events."
Sanjay Chandra, CIO at TiVo

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...