Operations | Monitoring | ITSM | DevOps | Cloud

Edwin AI’s Agent Orchestrator keeps incident investigation, context, and response aligned as work moves across tools, eliminating the manual handoffs that slow resolution. Every major incident has two timelines running in parallel. The first is the incident itself—services degrading, users affected, business impact accumulating. The second is quieter and just as costly: engineers switching tabs, re-explaining context to new responders, moving notes from one tool to another by hand.

Stop looking at the highway (the network) and start looking at the cars (the transactions).

The leaders responsible for modern IT environments rarely talk about features first. They talk about responsibility. In conversations at Nexus Live 2025, ScienceLogic’s annual customer conference, executives and architects across healthcare, federal systems, managed services, telecom, and enterprise IT described modernization not as a tooling upgrade, but as an escalation of accountability.

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.

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.

Last week was a great reminder for me about the challenges of the traditional model of observability defined by the “three pillars” of metrics, logs, and traces. One of the customers I’m currently working with is a large financial institution that has a robust three pillar implementation. Every critical application ships their telemetry to either or both their cloud-native tool and a central tool.

If you’ve spent time in industrial environments, you know the problem isn’t a lack of data. It’s collecting it reliably, contextualizing it, and storing it at scale. Most stacks weren’t built to fight all three battles.

Most cloud cost dashboards are built for one audience. The finance team wants to see totals by department. The engineering team wants to see costs by service. The DevOps team wants to see environment-level breakdowns. When everyone looks at the same dashboard, nobody gets what they actually need. This is where tailored cloud cost visibility starts to matter. When a team can see its own costs clearly, it moves faster, takes ownership, and starts treating cost data like it actually matters.

The expectations placed on infrastructure leaders have shifted in a way that is subtle on the surface but significant in practice, and much of that shift comes down to infrastructure cost visibility. Reliability and performance still matter, but they are no longer the differentiators they once were. Most enterprise environments are stable by design, and uptime is assumed. What has changed is the level of scrutiny around cost and decision-making.