July 2026 Summaries

As .NET Multi-platform App UI (MAUI) emerges as the standard for cross-platform UI development within the Microsoft ecosystem, its adoption for building iOS and Android applications has not been matched by advances in observability. Developers often face challenges with unsupported community bindings or maintaining their own SDK wrappers, compounded by the discontinuation of Microsoft Visual Studio App Center. Datadog addresses these issues with its official .NET MAUI SDK, which facilitates application instrumentation through a single supported NuGet package, offering features such as crash reporting, error tracking, network monitoring, and Session Replay. This SDK captures telemetry data across managed and native application layers, providing insights into crashes, user sessions, and performance, while integrating seamlessly into existing Datadog views for comprehensive analysis. Automatic instrumentation and deobfuscation of stack traces enhance engineers' ability to investigate and resolve issues efficiently. The SDK's capabilities extend to tracking user interactions and network requests, providing a unified dataset that aids in performance analysis across multiple platforms, thereby offering a reliable path for monitoring .NET MAUI applications without the need for custom solutions.

AI Guard is a security solution developed by Datadog for AWS Strands Agents to address the dynamic security risks associated with AI agents that can reason through tasks, call tools, and adapt based on intermediate results. By integrating with the Strands plugin, AI Guard evaluates prompts, model responses, and tool interactions during the agent's runtime, allowing it to monitor or block unsafe behavior without embedding security checks throughout the application code. This system registers callbacks on Strands life cycle events and assesses interactions in context, enabling the detection of multistep attacks such as prompt injection, data exfiltration, and tool misuse. AI Guard operates in both a monitoring mode and a blocking mode, allowing teams to adjust security policies without editing the agent code or redeploying applications, making it easier to manage security across various environments. Datadog provides detailed insights into the evaluations with traceable events and aggregate views, helping teams investigate potential security breaches while ensuring compliance with established policies.

DASH 2026, held in New York City, brought together thousands of technology professionals for 2½ days of sessions, workshops, and demos focused on modern systems' building, operating, and securing. Keynote announcements highlighted Datadog's AI-driven features like Bits Detection for autonomous monitoring and Bits Remediation for issue fixing, alongside tools for AI workload monitoring and network management. More than 100 technical sessions covered topics such as AI innovation's impact on engineering, with industry leaders and customers like OpenAI and Samsung sharing insights. Hands-on workshops and expo theaters offered practical guidance on Datadog's platform, while the Security Zone provided immersive experiences to enhance security skills. The event also featured regional delegations, a Women in Tech panel discussing AI's role in the industry, and the fifth annual DASH Partner Summit, which celebrated partnerships and strategic growth in observability, security, and AI.

Apple's platform ecosystem, including watchOS and visionOS, is experiencing growth as developers create applications for the Apple Watch and Apple Vision Pro, yet these platforms lack mature observability tools compared to iOS. Datadog Real User Monitoring (RUM) addresses this gap by providing comprehensive visibility into app behavior, supporting crash reporting, error tracking, and session-level observability for both platforms without requiring a separate SDK. It extends the dd-sdk-ios package to ensure compatibility and testing on watchOS and visionOS, enabling developers to monitor crashes with deobfuscated stack traces, identify runtime errors in WatchKit and SwiftUI apps, and analyze user interactions. Datadog's symbolication platform enhances error resolution by collecting system symbols, allowing developers to use existing workflows for tracking and resolving issues. This integration allows teams to gain insights into user experience and enhance app reliability on these evolving platforms.

Datadog's Static Code Analysis enhances static application security testing (SAST) by incorporating agentic evaluation and Bits Memories, which aim to reduce false positives and improve the accuracy of security assessments. Agentic evaluation allows for a comprehensive analysis of findings by examining repository-wide context, tracing related code paths, and assessing validators to determine whether vulnerabilities are genuine. Bits Memories adds another layer by integrating organizational knowledge and historical false positive reports, providing a more informed context for evaluations. This approach helps security teams prioritize vulnerabilities by distinguishing real threats from false alarms, thereby optimizing developer efforts and improving remediation processes. These features, when used together, offer a holistic view that mirrors human evaluators' depth of analysis, combining code repository evidence with institutional memory to deliver accurate security insights.

Datadog's exploration into experimentation reveals the challenges and strategic insights offered by understanding the effect distribution in experimental programs. While standard practices such as setting sample sizes, waiting for statistical significance, and maintaining a 95% confidence level are followed, the aggregate analysis of multiple experiments often uncovers interpretative pitfalls, primarily due to the inherent 5% chance of false positives under the null hypothesis. The true effect distribution provides a more accurate understanding of experiment outcomes, counteracting the inflated expectations created by the observed effects, which can mislead decision-making and resource allocation. By estimating the effect distribution, teams can better gauge the realistic magnitude of true effects and thereby improve the design and impact of their experiments. This approach not only mitigates errors like the winner's curse and Type S errors but also enhances decision-making through concepts like the expected value of sample information (EVSI), which quantifies the value of information gained from experiments. Moreover, analyzing separate effect distributions for different experiment categories can guide resource allocation more effectively, as seen in the comparison of customer service versus search ranking experiments. Understanding and applying effect distributions transform experimentation from isolated decisions to a strategic asset, offering significant insights regardless of the statistical framework employed.

Datadog Incident Response introduces three AI-powered features to streamline incident investigation and coordination, enhancing the efficiency of engineering teams. The "Bits Investigation" acts as an AI responder, analyzing the same context as human teams to develop hypotheses and identify root causes without assumptions, while providing updates and summaries in real-time. AI-generated chat summaries offer quick context for responders joining incidents in progress, reducing the time needed to catch up by synthesizing ongoing remediation work and key developments. Additionally, the integration with video conferencing tools allows automatic capture and summarization of bridge call discussions, ensuring critical decisions and plans are documented within the incident timeline. These capabilities unify data and communication across platforms, minimizing time spent on information gathering and improving overall incident management effectiveness.

Datadog has been leveraging AI tools to enhance its software development life cycle (SDLC), resulting in significant improvements in internal tooling and processes. One of their key initiatives includes creating an automated AI support application using Gas City to streamline the management of deployment support requests, which has notably increased the number of resolved requests per shift. Additionally, Datadog has developed a shadowing platform using Claude Code and Cursor to test backend query changes against real-world conditions, allowing for comprehensive validation and reducing guesswork. Another innovation involves optimizing memory allocation in their Go services through the implementation of a new parser designed with Claude Code, which led to a 10% increase in network capacity and potential annual savings of $2 million. These projects underscore Datadog's commitment to using AI to foster more efficient development workflows and enhance the overall performance and reliability of their systems.

Datadog's Data Completeness team has developed a robust system to ensure the integrity and completeness of data across its vast distributed ingestion pipelines, which handle billions of payloads per second. This system is crucial for maintaining the reliability of automated decisions and customer-facing dashboards, as incomplete data can lead to flawed outcomes. To achieve this, the team tracks data completeness by segmenting pipelines and monitoring payloads as they traverse each segment, using create and acknowledgment events to gauge completeness. By employing a time-bucket model, the system ensures idempotency and minimizes external dependencies, allowing it to remain functional even during system degradations. Additionally, a load-shedding mechanism dynamically adjusts sampling to maintain accuracy without incurring prohibitive costs. The completeness system is designed to be resilient, deploying independently across multiple availability zones and employing custom in-memory storage to handle the vast data volumes efficiently. By integrating metadata for real-time topology insights and facilitating incident response, Datadog has created a system that not only detects and mitigates pipeline issues swiftly but also supports ongoing automation and scalability efforts.