Operations | Monitoring | ITSM | DevOps | Cloud

As organizations grow, they face increasing difficulty in managing their observability efforts. More teams mean more dashboards, monitors, API keys, pipelines, and custom configurations. Without a centralized view, administrators spend hours chasing down untagged resources, investigating surprise bills, and revoking dormant credentials. Governance becomes a reactive effort to reduce waste and address issues, falling short of its potential to proactively create standards and optimize observability.

Technical teams want to know the newest, most cutting-edge tools they can implement to give themselves a competitive advantage, whether it’s the latest developer framework or modern CI/CD practices that boost velocity. But there’s one tool from all the way back in the 1920s that can improve any organization, no matter its scale: the randomized, controlled trial—or simply put, experiments.

As organizations continue to heavily invest in AI and build more agentic workflows, their telemetry data volumes can surge quickly, and the associated costs can become unpredictable. To regain control of their data, many AI-forward teams are turning to high-throughput, low-latency pipelines to collect and route data to tools such as OpenTelemetry (OTel) and ClickHouse. But these self-hosted solutions come with drawbacks.

Single Step Instrumentation (SSI) simplifies Datadog Application Performance Monitoring (APM) by automatically discovering and instrumenting services across a host. For many teams, SSI is the ideal starting point because it helps them achieve full visibility with minimal setup. However, as environments grow, teams often want more control over which services get traced. Auxiliary workloads such as batch jobs and cron tasks might not require distributed tracing.

If you’re building LLM-powered applications and agents, you’ve probably asked yourself: “How do I know if my changes actually made things better?” You can tweak prompts, adjust temperature settings, or try different models, but it’s not always easy to validate whether version B’s response is better than version A’s. Most teams fly blind in preproduction and rely on user feedback to see how well their application works in the real world.

Boomi is an Integration Platform as a Service (iPaaS) used by thousands of organizations to connect applications, data, and workflows across cloud and on-premises environments. Business-critical processes, from order fulfillment pipelines to customer data synchronization, depend on Boomi Atoms and Molecules running reliably.

When engineers investigate SQL queries, they normally think of index scans as a fast and efficient step in the query’s execution plan. When executed correctly, they fetch only the relevant rows from your table as opposed to sequential scans that read the entire table, reducing latency and query costs. However, just because an execution plan uses an index scan doesn’t mean that the scan is fast or performant.

Platform engineering teams have access to hundreds of metrics, yet over 40% of platform initiatives cannot demonstrate measurable value within the first year. Teams that cannot quantify their impact fail to obtain executive sponsorship, risk being defunded, and ultimately, face deprecation. To accurately calculate a platform’s ROI, platform engineering teams need to differentiate between signals that measure platform effectiveness and those that should be used solely for investigative purposes.

Recorded Future provides real-time threat intelligence about indicators of compromise (IOCs), including malicious IP addresses, domains, and vulnerabilities. It also adds context on threat actors and campaigns to help security teams understand which signals represent real risk and prioritize their responses accordingly.

This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA. Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change.