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By Annie Freeman
Agentic coding tools like Claude Code and Codex have taken centre stage and inserted themselves into the critical path of software development. This shift has happened fast, and for most teams, the visibility hasn’t caught up. Until now we’ve been evaluating our vibe coding the same way – on vibes. You might say “this feels faster” or “that seems like a better approach”. That’s not going to scale.
Kotak811, the digital-first engine of Kotak Mahindra Bank, is a banking platform serving over 23 million users across India. Since its launch in 2017, Kotak811 has transformed into the bank’s primary growth driver, now accounting for 70% of all new customer acquisitions. The platform is widely recognized for offering a paperless, mobile-first experience, providing everything from instant zero-balance accounts to seamless UPI payments and investment tools.
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By Micha Duman
Many organizations deploying AI are learning similar lessons right now: the challenge isn’t this or that AI model, it’s the data. According to Gartner, 60% of AI projects will be abandoned by organizations because of failures to support these projects with AI-ready data. Also, 63% of organizations either lack or aren’t sure they have the right data management practices to get there.
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By Lily Waldorf
For those of us using tools like Claude Code, Codex, or Gemini, we already know they’re powerful. They can write code, refactor functions, open PRs, even run commands. For a lot of developers, they’re already part of the daily workflow. But once you zoom out beyond the individual developer, the biggest problem isn’t productivity. It’s control. AI coding tools are powerful, but they introduce a new, unpredictable cost layer that most teams don’t fully understand.
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By Micha Duman
The real economic decision for observability happens at ingest, before storage, billing, and retention choices are locked-in. Until now, the logic governing that decision could only see three broad fields: application, subsystem, and severity. That just changed. TCO routing now matches on any field in the event payload, including nested keys, custom fields, and event body content, using DPXL, the DataPrime Expression Language.
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By Lily Waldorf
Most observability platforms are built to answer one question: what’s broken right now. Regulators are asking a different one: what happened, exactly, and can you prove it? Digital banking operates under constant regulatory scrutiny, where frameworks like DORA, PCI-DSS, and GDPR require every incident to be fully reconstructed across systems, timelines, and access. Systems can recover quickly, but the ability to explain what happened often remains fragmented across tools and teams.
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By Ido Golan
It is 2 AM. Someone on-call gets paged. Conversion rates on the checkout page dropped 30 percent in the last hour. The immediate questions are familiar. Is this a JavaScript error? A slow API call? A broken third-party script? A performance regression that never throws an exception but quietly drives users away? In most teams, answering those questions is not hard because the data is missing. It is hard because the investigation is split across too many places.
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By Jonny Steiner
Traditionally, achieving deep visibility into distributed systems required significant trade-offs in engineering time. Collecting meaningful application metrics and traces required teams to embed language-specific agents, modify source code, or manage complex library dependencies across every service.
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By Lily Waldorf
Capital markets systems don’t scale linearly. A macro event, an earnings release, a sudden liquidity shift, and telemetry volume doubles in seconds. In most observability platforms today, that spike means one thing: every byte gets written to a high-cost index before a single query can touch it. There’s no middle ground. You pay full indexing cost for the compliance log that no one queries for six months, the same way you pay for the execution trace you need right now.
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By Lily Waldorf
Digital trading firms operate in environments where milliseconds determine profit and loss. During volatile market conditions, platforms can appear fully operational while execution quality quietly degrades. When prices shift in so quickly, even a minor drift in your order-routing path means your competitors are exploiting the delta, while your platform appears perfectly green. For trading firms, observability is not just about uptime.
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By Coralogix
AI is only as useful as the context you give it. An autonomous observability agent can unlock serious value from your telemetry, but only when the foundation is right: good telemetry, a strong data layer, and efficient access to the data. Annie Freeman and Lewis Isaac had a lot to say about this at AWS Summit London this week! hashtag#Observability hashtag#AI hashtag#AWSSummitLondon hashtag#DevOps hashtag#OpenTelemetry.
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By Coralogix
What happens when 20,000 engineers descend on Amsterdam to talk about Kubernetes and AI? Welcome to Episode 1 of Live Laugh Logs, the podcast from Annie, Lewis and Andre from the Coralogix Developer Relations team where we will get together and recap everything going on in our worlds! We had an amazing time at KubeCon in Amsterdam and had loads of insights from the talks we went to around designing observability systems, all the AI tools being created and how to observe them, and using agent-generated code.
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By Coralogix
Stop the "Scavenger Hunt" during incidents. In this video, we walk through the new Coralogix Trace Drilldown, now GA for all customers. Learn how to move from high-level trace views to deep span insights in a single, unified workspace—without ever losing context. Whether you're investigating a latency spike or a failing microservice, the Trace Drilldown helps you answer "Where is the bottleneck?" from three different perspectives in one frame. What you’ll learn.
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By Coralogix
Transform millions of spans into a clear visual map. In this demo, we use Coralogix Trace Highlights to isolate a performance regression and pivot from 400k spans down to the exact root cause in just a few clicks.
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By Coralogix
In this video, we introduce Fleet Management and how it helps teams control their telemetry estate as it scales. See how you can centrally manage collectors and agents, standardize configurations across environments, and roll out updates confidently, reducing operational effort and risk.
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By Coralogix
Olly is Coralogix’s AI-native observability agent that makes observability data fast, accessible, and actionable—for everyone. Traditionally, teams have spent valuable time piecing together dashboards and writing queries to troubleshoot issues. Olly changes that by letting you ask real questions in natural language and delivering instant, intelligent answers from across your logs, metrics, and traces.
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By Coralogix
Are you struggling to define reliability targets? Teams nowadays are turning to Service Level Objectives (SLOs), reliability targets that can be used to define how much you can play around with your systems before users are affected too much. While they're a great way of defining reliability targets, they are difficult to manage. That's why we built the SLO Center. One place to define, track, zoom into, and stay on top of all your reliability targets and error budgets - so you can be sure when you can experiment, and when it's best to stay safe.
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By Coralogix
Debug faster, improve application performance, and lower your cloud costs - without slowing down production. Traditional profiling solutions come with a heavy price—added latency, excessive resource consumption, and performance degradation. At, we’re changing the game with Continuous Profiling, the first of its kind to offer real-time, kernel-level visibility into application performance without any code changes or production impact.
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By Coralogix
Traditional observability wasn't built for. The reason? AI operates in shades of grey, where outcomes are non-deterministic. That's why we built the AI Center, bringing real-time AI observability to thousands of enterprises worldwide. As part of our AI Center, we built an evaluation engine, designed to oversee and detect specific issues that are most common when building AI agents. Teams can choose the evaluators they want to oversee each agent and receive live alerts and reports into specific quality, security and compliance issues.
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By Coralogix
There are numerous types of logs in AWS, and the more applications and services you run in AWS, the more complex your logging needs are bound to be. Learn how to manage AWS log data that originates from various sources across every layer of the application stack, is varied in format, frequency, and importance.
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Coralogix helps software companies avoid getting lost in their log data by automatically figuring out their production problems:
- Know when your flows break: Coralogix maps your software flows, automatically detects production problems and delivers pinpoint insights.
- Make your Big Data small: Coralogix’s Loggregation automatically clusters your log data back into its original patterns so you can view hours of data in seconds.
- All your information at a glance: Use Coralogix or our hosted Kibana to query your data, view your live log stream, and define your dashboard widgets for maximum control over your data.
Our machine learning powered platform turns your cluttered log data into a meaningful set of templates and flows. View patterns and trends, and gain valuable insights to stay one step ahead at all times!