Causely launches a new integration with Grafana Labs that automatically surfaces root causes directly within Grafana dashboards.
The Causely platform utilizes purpose-built causal models to infer root causes automatically, saving engineers hours of manual correlation and guesswork.
“Grafana has millions of users and some of the most well-known companies using their cloud solutions to drive their digital businesses,” said Yotam Yemini, CEO of Causely. “They are servicing massive companies like Atlassian, Dell, Roblox and Wells Fargo – which means a tremendous amount of data is being generated that needs to be made sense of. Causely automatically identifies the root cause of anomalies and instantly makes all the data shown in a customer’s Grafana dashboards more actionable.”
By embedding Causely’s intelligence directly into a Grafana dashboard, engineers can instantly see the “why” behind performance issues in the context of their services, significantly cutting resolution time when there’s an alert that needs to be addressed. Causely also plugs into Grafana Alertmanager, enriching existing alerts with real-time, continuously-updated-root-cause intelligence. This AI-powered capability goes beyond sending alerts when something is wrong, getting deeper into where the problem originated and what to do next within the incident response workflow.
“By integrating Causely with Grafana, engineers have another option to see the inferred root causes of issues amongst the context of all of their relevant services,” said Ash Mazhari, VP of Corporate Development at Grafana Labs. "We're thrilled to collaborate with Causely to offer our users more choices and enhance their insights and value."
The Causely system works by automatically mapping an application’s topology and service dependencies, then applying a targeted set of high-probability root causes to this data. This novel approach to observability significantly reduces manual troubleshooting by making sense of patterns and determining the best path to remediation, converting alerts into actionable root causes.
The Latest
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 ...
For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...
Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...