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
Gartner identified the top data and analytics (D&A) trends for 2025 that are driving the emergence of a wide range of challenges, including organizational and human issues ...
Traditional network monitoring, while valuable, often falls short in providing the context needed to truly understand network behavior. This is where observability shines. In this blog, we'll compare and contrast traditional network monitoring and observability — highlighting the benefits of this evolving approach ...
A recent Rocket Software and Foundry study found that just 28% of organizations fully leverage their mainframe data, a concerning statistic given its critical role in powering AI models, predictive analytics, and informed decision-making ...
What kind of ROI is your organization seeing on its technology investments? If your answer is "it's complicated," you're not alone. According to a recent study conducted by Apptio ... there is a disconnect between enterprise technology spending and organizations' ability to measure the results ...
In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks ...
In MEAN TIME TO INSIGHT Episode 12, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses purchasing new network observability solutions....
There's an image problem with mobile app security. While it's critical for highly regulated industries like financial services, it is often overlooked in others. This usually comes down to development priorities, which typically fall into three categories: user experience, app performance, and app security. When dealing with finite resources such as time, shifting priorities, and team skill sets, engineering teams often have to prioritize one over the others. Usually, security is the odd man out ...
IT outages, caused by poor-quality software updates, are no longer rare incidents but rather frequent occurrences, directly impacting over half of US consumers. According to the 2024 Software Failure Sentiment Report from Harness, many now equate these failures to critical public health crises ...
In just a few months, Google will again head to Washington DC and meet with the government for a two-week remedy trial to cement the fate of what happens to Chrome and its search business in the face of ongoing antitrust court case(s). Or, Google may proactively decide to make changes, putting the power in its hands to outline a suitable remedy. Regardless of the outcome, one thing is sure: there will be far more implications for AI than just a shift in Google's Search business ...
In today's fast-paced digital world, Application Performance Monitoring (APM) is crucial for maintaining the health of an organization's digital ecosystem. However, the complexities of modern IT environments, including distributed architectures, hybrid clouds, and dynamic workloads, present significant challenges ... This blog explores the challenges of implementing application performance monitoring (APM) and offers strategies for overcoming them ...