
Datadog announced the launch of Live Debugger, a new tool that enables developers to step through code in production environments and find the exact root cause of coding errors.
Live Debugger requires no downtime and enables developers to work directly in production environments instead of spending countless hours of trial and error to reproduce production issues in development environments.
Live Debugger aggregates the necessary information from the live production environment and integrating it directly into the user’s Integrated Development Environment (IDE). The product accelerates root-cause analysis with AI-generated exception summaries and one-click test creation to accurately reproduce all bug conditions based on production data. Using Live Debugger not only improves the developer experience, it also dramatically reduces the time it takes to resolve issues, freeing up engineers to spend more time delivering business value.
“Debugging can be a slow and inefficient process which requires extensive manual data collection and the ability to reproduce bugs in perfectly reconstructed conditions. These constraints negatively impact developer productivity and, ultimately, the end user experience,” said Hugo Kaczmarek, Director of Product at Datadog. “With today’s launch, we are taking the guesswork out of debugging, minimizing the friction experienced by developers and creating a tool that inherently supports rapid issue resolution while maintaining the highest standards of code quality and security.”
Features of Live Debugger include:
- Exception Replay: Developers can step through the execution flow of their code and see local variable values that were captured live when the exception was thrown—all without needing to run code.
- Powerful Visualizations and Context: Datadog’s unified platform delivers the observability context needed to troubleshoot issues quickly and provides an AI-powered summary of the code’s executional context, a starting hypothesis for the root cause of the issue, and visualizations of data flows between services and where the interaction between them occurred in the code.
- Integrated AI-Generated Tests: Teams can quickly and accurately reproduce issues by using production data to mock all relevant values across dependent microservices. Tests can then be run directly in the customer’s IDE with just one click.
Live Debugger is available in beta now.
The Latest
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...