BigPanda announced the BigPanda agentic IT operations platform.
This powerful new set of capabilities is designed to help enterprises automate the manual and time-intensive workflows of ITOps and incident management.
This launch introduces two AI solutions:
- BigPanda AI Detection and Response: An autonomous AI agent that is purpose-built to automate the manual detection and response work of Level 1 (L1) operations teams.
- BigPanda AI Incident Assistant: A real-time decision and automation partner for incident response teams.
Both solutions are powered by the BigPanda IT Knowledge Graph, which is purpose-built to enable an AI-first ITOps data strategy. The IT Knowledge Graph unifies structured IT data with unstructured organizational and human knowledge, such as chat and bridge call transcripts, that is often buried and siloed. This dynamic, intelligent data model forms the core of the BigPanda agentic IT operations platform. This intelligence allows enterprises to connect and automate the entire incident lifecycle across fragmented teams, including centralized Ops and decentralized DevOps teams.
“There are $200 billion worth of manual ITOps workflows that are ripe for intelligent automation,” said Assaf Resnick, CEO of BigPanda. “With agentic ITOps, we’re helping enterprises move beyond manual, slow incident management toward intelligent systems that free up talent and reduce operating costs.”
AI Detection and Response (ADR) automates L1 incident workflows by detecting issues early, diagnosing and investigating them with AI, and responding with machine precision. ADR continuously learns from operational history and real-time human feedback, and doesn’t require manual data curation or rules maintenance.
“Agentic IT operations is a complete reimagining of the L1 function,” said Fred Koopmans, Chief Product Officer at BigPanda. “Our AI doesn’t just detect, it understands. It acts. And most importantly, it learns from every incident to improve over time.”
AI Incident Assistant acts as a real-time AI partner for incident commanders, L2 engineers, SREs, and service owners to investigate and resolve incidents faster. It surfaces hidden institutional knowledge across siloed teams, tools, and systems to quickly uncover what's happening, why, and how to fix it.
Teams can now reduce reliance on long bridge calls, eliminate unnecessary escalations, and generate lasting institutional knowledge from untapped sources like conversations, tickets, and chat threads.
“AI Incident Assistant helps our customers eliminate the chaos of complex incidents,” said Koopmans. “Instead of scrambling for answers, response teams get agentic support that anticipates needs, suggests actions, and documents outcomes.”
At the core of the BigPanda platform is the IT Knowledge Graph, a real-time intelligence engine that continuously ingests and connects data that is buried and siloed across a large enterprise. This includes various types of structured and unstructured data, such as machine telemetry, service tickets, change data, collaboration artifacts, runbooks, chat histories, and bridge call transcripts. Legacy IT systems and processes require highly structured and curated data. The IT Knowledge Graph uses AI agents that can learn from messy, incomplete, and informal inputs, mirroring how real-world teams operate.
“Traditional IT systems & processes only act on a narrow slice of observability or IT service management data,” said Resnick. “The IT Knowledge Graph gives our platform a panoramic view. It sees across silos, learns from institutional experience, and adapts in real-time, even when the data isn’t perfect.”
This breadth of context enables BigPanda to support use cases that traditional IT solutions simply can’t – from identifying incidents missed by monitoring to recommending resolutions drawn from chat threads and unlogged fixes.
The BigPanda agentic IT operations platform helps enterprises significantly reduce operational costs by targeting:
- High personnel costs that are driven by manual L1/L2 workloads and preventable escalations.
- High MSP spend due to incentives based on ticket volume, not incident reduction.
- Disruption of strategic resources, as valuable engineers are pulled into triage instead of innovation.
BigPanda AI Detection and Response is generally available today, with additional agentic AI and detection capabilities coming in Summer 2025.
BigPanda AI Incident Assistant is generally available today.
The Latest
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 ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.