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Augtera Networks Introduces Data Center Network AIOps Solution

Augtera Networks announced the result of 3 years of development and customer partnership, a holistic Network AIOps Data Center solution.

“Over the last three years we have partnered with some of the largest Data Center Network Operations teams to refine our solution,” said Rahul Aggarwal, Founder and CEO of Augtera Networks. “Our AI/ML algorithms have been specialized for Data Center networks and customers are seeing dramatic improvement in KPIs such as detection, mitigation, and repair. Most importantly, our technology reduces the total number of incidents that are actioned, resulting in operations teams not just running faster, but running smarter and more effectively.”

Augtera Networks Data Center Solution:

- Addresses the pain points, use cases APIs, ITSM integrations, Equipment/Vendor integrations, data types and constructs specific to Data Centers.

- Proactive detection of environmental and optical degradation

- Anomaly detection for aggregates such as a POD, fabric, or Data Center interconnects

- Fabric, server, and Hybrid Cloud, latency, and loss anomaly detection

- Flow analysis including Hybrid Cloud

- Fabric congestion impact on application sessions

- VXLAN and EVPN underlay / overlay insights including ECMP analysis

- Firewall and Load Balancer anomalies

- Multi-vendor support including Arista Networks, Cisco Systems, Juniper Networks, Dell Enterprise SONiC, F5, Palo Alto Networks, VMWare, and any equipment using industry-standard interfaces

- Integrations including Amazon Web Services, Azure, Google Cloud Platform, ServiceNow, and Slack.

The solution includes capabilities that come standard with all Augtera Networks solutions including:

- Holistic data ingestion

- Automated creation of operationally relevant trouble tickets

- Policy-driven auto-correlation and noise elimination

- AI/ML-based anomaly & gray failure detection

- Topology auto discovery

- Multi-layer, topology-aware, auto-correlation

- Topology-mapped “Time-machine” visualization of metrics, events, & anomalies

- Real-Time Syslog anomaly detection including Zero-Day anomalies

- DevOps friendly APIs

Modern Data Center network architectures have simplified the hardware environment while increasing operations complexity. Operations teams can no longer simply run faster, they cannot find or economically afford enough people to keep up. They must change the way they work, dramatically reducing the number of incidents that are ticketed / actioned.

This requires attention to workflows, and a multi-layer, multi-vendor understanding of networking. Most importantly, it requires investing time partnering with Data Center teams to develop the needed solution. Only Augtera Networks has done all this in a holistic way.

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Augtera Networks Introduces Data Center Network AIOps Solution

Augtera Networks announced the result of 3 years of development and customer partnership, a holistic Network AIOps Data Center solution.

“Over the last three years we have partnered with some of the largest Data Center Network Operations teams to refine our solution,” said Rahul Aggarwal, Founder and CEO of Augtera Networks. “Our AI/ML algorithms have been specialized for Data Center networks and customers are seeing dramatic improvement in KPIs such as detection, mitigation, and repair. Most importantly, our technology reduces the total number of incidents that are actioned, resulting in operations teams not just running faster, but running smarter and more effectively.”

Augtera Networks Data Center Solution:

- Addresses the pain points, use cases APIs, ITSM integrations, Equipment/Vendor integrations, data types and constructs specific to Data Centers.

- Proactive detection of environmental and optical degradation

- Anomaly detection for aggregates such as a POD, fabric, or Data Center interconnects

- Fabric, server, and Hybrid Cloud, latency, and loss anomaly detection

- Flow analysis including Hybrid Cloud

- Fabric congestion impact on application sessions

- VXLAN and EVPN underlay / overlay insights including ECMP analysis

- Firewall and Load Balancer anomalies

- Multi-vendor support including Arista Networks, Cisco Systems, Juniper Networks, Dell Enterprise SONiC, F5, Palo Alto Networks, VMWare, and any equipment using industry-standard interfaces

- Integrations including Amazon Web Services, Azure, Google Cloud Platform, ServiceNow, and Slack.

The solution includes capabilities that come standard with all Augtera Networks solutions including:

- Holistic data ingestion

- Automated creation of operationally relevant trouble tickets

- Policy-driven auto-correlation and noise elimination

- AI/ML-based anomaly & gray failure detection

- Topology auto discovery

- Multi-layer, topology-aware, auto-correlation

- Topology-mapped “Time-machine” visualization of metrics, events, & anomalies

- Real-Time Syslog anomaly detection including Zero-Day anomalies

- DevOps friendly APIs

Modern Data Center network architectures have simplified the hardware environment while increasing operations complexity. Operations teams can no longer simply run faster, they cannot find or economically afford enough people to keep up. They must change the way they work, dramatically reducing the number of incidents that are ticketed / actioned.

This requires attention to workflows, and a multi-layer, multi-vendor understanding of networking. Most importantly, it requires investing time partnering with Data Center teams to develop the needed solution. Only Augtera Networks has done all this in a holistic way.

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In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...