Skip to main content

Lightrun Adds Ability to Dynamically Pull Telemetry Data from Live Apps

Lightrun has added an ability to dynamically pull missing telemetry evidence from live application environments without having to deploy additional instrumentation to its namesake site reliability engineering (SRE) platform that is based on artificial intelligence (AI).

Company CEO Ilan Peleg said the Lightrun AI SRE platform includes a sandbox deployed via a software development kit (SDK) that can now be integrated with a live application environment to collect new evidence, test hypotheses, and validate outcomes against real execution behavior without having to deploy additional agents to collect telemetry data.

The overall goal is to provide DevOps teams with much-needed additional context on demand to reduce mean time to detection of the root cause of an incident, he added.

That capability will soon prove to be crucial as the volume of applications that are being deployed in the age of AI begins to overwhelm the ability of DevOps teams to manage incidents, noted Peleg.

At the core of the Lightrun platform is a Runtime Context engine that enables DevOps teams to understand how code truly behaves. Armed with those insights, it becomes possible to both identify issues and bottlenecks before and after an application is deployed in a production environment, he added. In fact, DevOps teams can now run validated code changes that eliminates guesswork and rollbacks, noted Peleg.

Rather than relying on observability platforms designed for IT operations teams, Lightrun is instead making a case for using an SDK combined with an AI model to enable developers to observe code in a way that fits naturally within an existing workflow.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said the runtime context engine developed by Lightrun creates a foundation for validated incident investigation. Collecting telemetry on demand from live production environments without additional instrumentation, changes how DevOps teams manage incident response, he added. As AI accelerates deployment velocity, evidence gaps compound; teams cannot validate hypotheses against real execution behavior when instrumentation lags behind deployment cycles noted Ashley.

Testing against live execution behavior also reduces mean time to root cause and eliminates the rollback cycles that follow from incomplete production visibility, he said.

It’s not clear to what degree that approach might reduce the need for other types of observability platforms, but at the very least developers will now be able to debug runtime environments without always waiting for guidance to be provided by an IT operations team. That approach should reduce the number of issues that DevOps engineers would need to directly address after an application has been deployed in a production environment.

Like it or not, the AI genie, as far as software engineering is concerned, is out of the proverbial bottle. The amount of code moving through DevOps pipelines will inevitably increase to a point where without the help of AI tools and platforms to manage it, DevOps teams will soon be overwhelmed.

DevOps teams, by now, should be identifying bottlenecks in their existing engineering workflows that are likely to only become bigger as the volume of code moving through pipelines increases. Ultimately, the AI issue will come down to determining to what degree the tools and platforms being relied on today to manage those pipelines might need to be upgraded or replaced to accommodate all the code being generated by developers in the age of AI.



from DevOps.com https://ift.tt/jrNy21D
Labels:

Comments

Post a Comment

Popular posts from this blog

Cursor’s New SDK Turns AI Coding Agents Into Deployable Infrastructure

For most of its life, Cursor has been an IDE. A very good one. But with the public beta of the Cursor SDK, the company is making a different kind of move — one that should get the attention of DevOps teams. The Cursor SDK is a TypeScript library that gives engineers programmatic access to the same runtime, models, and agent harness that power Cursor’s desktop app, CLI, and web interface. In short, the agents that used to live inside an editor can now be invoked from anywhere in your stack. That’s a meaningful shift in how AI coding tools fit into software delivery pipelines. From the Editor to the Pipeline If you’ve used Cursor before, the workflow is familiar — you interact with an agent in real time, asking it to write functions, fix bugs, or review code. The SDK breaks that dependency on interactive use. Now you can call those same agents programmatically, from a CI/CD trigger, a backend service, or embedded inside another tool. Getting started is a single inst...
Post a Comment
Read more

Mistral Moves Coding Agents to the Cloud — and Gets Out of Your Way

For the past year or so, AI coding agents have been tethered to your local machine. You kick off a task, watch the terminal, and babysit every step. It works — but it’s not exactly hands-free. Mistral just changed that. On April 29, the Paris-based AI company announced remote coding agents for its Vibe platform, powered by a new model called Mistral Medium 3.5. The idea is simple: Instead of running coding sessions on your laptop, they now run in the cloud — asynchronously, in parallel, and without you watching over them. What’s Actually New Coding sessions can now work through long tasks while you’re away. Many can run in parallel, and you no longer become the bottleneck at every step the agent takes. That’s the core pitch. You start a task from the Mistral Vibe CLI or directly from Le Chat — Mistral’s AI assistant — and the agent handles the rest. When it’s done, it opens a pull request on GitHub and notifies you, so you review the result inste...
Post a Comment
Read more

OpenAI Debuts Symphony to Orchestrate Coding Agents at Scale

OpenAI has unveiled Symphony, an open-source specification that shifts how software development teams deploy AI in workflows, moving from interactive coding assistance toward continuous orchestration of autonomous agents. Symphony reframes project management tools as operational hubs for AI-driven coding. Rather than prompting an assistant for individual tasks, developers assign work through issue trackers, allowing agents to execute tasks in parallel and deliver outputs for human review. The change reflects a trend in enterprise AI in which systems are increasingly embedded into production pipelines rather than used as standalone tools. Symphony emerged from internal experimentation at   OpenAI , where engineers attempted to scale the use of   Codex   across multiple concurrent sessions. While the agents proved capable, human operators became the limiting factor. Engineers found they could only manage a handful of sessions before coordination overhead offset pro...
Post a Comment
Read more