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
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 ...