Coralogix announced the commercial launch of Olly, an autonomous observability agent that independently investigates and surfaces production issues in real time.
Olly correlates telemetry data, runs analysis, and delivers clear, evidence-backed answers without requiring prompting.
Olly acts as a proactive intelligence layer that anticipates problems, adapts to context, and continually evolves with its users. It behaves like a true engineering teammate, deciding what to analyze, running the necessary queries, explaining every decision it makes, and offering next steps.
Olly removes the complexity of troubleshooting by autonomously identifying root causes, surfacing key signals, and detecting anomalies as they occur. It generates on-demand visualizations from live telemetry and provides precise, data-driven answers to questions like "What is frustrating my customers today?" During incidents, Olly pinpoints affected services, highlights critical bottlenecks, and recommends remediation steps, giving teams a dependable partner for seamless troubleshooting.
Traditional observability forces engineers to navigate countless dashboards and manually correlate logs, metrics, and traces, which often takes hours. Olly eliminates this problem by fully analyzing observability data points and correlating telemetry on its own, reducing investigation time from hours to minutes.
“Organizations are under tremendous pressure to deliver rapidly and at higher quality,” said Ariel Assaraf, CEO and Co-Founder, Coralogix. “Olly gives teams insights that weren't possible before, turning telemetry data into clear, reliable answers so businesses can ship faster and operate with far greater confidence.”
The Latest
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...
In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...
Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...
Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...