
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
80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...
40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...
Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...