Operations | Monitoring | ITSM | DevOps | Cloud

Unified Logs, Traces, and Errors: Why One Tool Beats Three

Last updated: July 2026 Your Rails app throws a 500. You open Sentry and find the exception. The stack trace points to a controller action, but it does not tell you why the database call failed. You switch to Datadog and search for the request trace. The trace shows a 3-second query, but you do not know what the application was logging at that moment. You open your log aggregator, paste in the request ID, and scroll through output until you find the slow query log line that explains the lock contention.

Build an SRE Agent Harness for AIOps Without Context Blowout

An agent harness for AIOps is the runtime layer that coding agents like Claude Code were never built to provide: context isolation, decision traceability, and gated execution for tools that touch production. Aura is Mezmo's open-source (Apache 2.0) agent harness, purpose-built for operations work rather than software development.

When and what should I be logging?

This is a follow-up to Sergiy’s post Errors, traces, logs, metrics: when to reach for what. Modern observability platforms, like Sentry, give developers a lot of choice. For a given problem, should you use traces, profiles, metrics, logs? If you take away one thing from this post, I hope it’s this: when in doubt, start by adding a few targeted log lines.

The future of governing AI agents

How to build governance into autonomous security agents from the architecture up The industry has moved fast on capabilities. Agents now triage alerts, investigate endpoints, create detection rules, and enrich indicators, and they are even capable of performing most actions we as security operators can perform. The architecture patterns are maturing, as are the models, but governance is not keeping pace.

Called it (mostly): Checking in on 2026 predictions so far

On this episode of Masters of Data, we revisit the predictions Adam White, Zoe Hawkins, and David Girvin made at the end of last year, checking our own scorecard halfway through 2026. The hits: agents running amok and deleting databases, MCP becoming the backbone for tracking what agents actually do, growing security gaps around personal data, and a collective rejection of low-quality AI content. The misses: we underestimated how fast companies would cut staff for AI, then quietly start rehiring once the agents couldn't cover the work, and we're still arguing about whether token burn is a cost problem or a coming attack vector.

MCP vs CLI: Does it even make a difference? | Live Laugh Logs ep. 3

MCP vs CLI: does it even make a difference? Here’s everything you need to know. Welcome to Episode 3 of Live Laugh Logs, the podcast from the Coralogix Developer Relations team. This week Andre has made the move to the US, so Annie and Lewis are joined by George Pickers, Head of Solution Engineering for EMEA & APAC at Coralogix.

Q&A: How Elastic and Anyshift are bringing AI-powered context to incident response

Incident response often depends on connecting two kinds of context: what changed in the environment and what the logs say happened next. Through a new integration with Elastic, Anyshift’s AI agent, Annie, can read from a customer’s Elasticsearch deployment to search logs, surface error and warning spikes, and correlate log evidence with infrastructure change history.

SLA vs SLO vs SLI Explained: What Should You Track?

In this video, learn the difference between SLA, SLO, and SLI and why understanding each one is essential for delivering reliable IT services. Discover how these three service level metrics work together and why tracking the right one helps improve service reliability, customer satisfaction, and operational performance. Whether you're an IT operations professional, SRE, DevOps engineer, or service manager, this video explains SLA, SLO, and SLI in simple terms so you can build measurable goals and realistic service commitments.

Tech Talk: Observability Simplified, APM and Network Behavior

Participants are welcomed to a session titled "Observability Simplified," focusing on user experience, application performance, and network behavior. This second part of a three-part series highlights how the Splunk Observability Cloud and Cisco ThousandEyes can create a unified view of applications, infrastructure, and network performance. Key discussions include addressing siloed troubleshooting, enhancing visibility, and a live demo showcasing how to identify network issues affecting application performance. Attendees are encouraged to participate in the Q&A and are reminded that the session will be recorded for future reference.

What Is NetFlow, and How Does It Reveal Where Traffic Goes?

In this video, learn what NetFlow is and why it's one of the most effective technologies for understanding network traffic. Discover how NetFlow goes beyond basic bandwidth monitoring by showing who is using your network, what applications are consuming bandwidth, and how traffic patterns change over time. Whether you're a network administrator, IT operations engineer, or infrastructure manager, this video explains NetFlow in simple terms and shows how it helps identify bandwidth hogs, troubleshoot slow networks, and make smarter capacity planning decisions.

A Four-Step Blueprint for Faster Root Cause Analysis: A Logz.io Webinar

Incident investigations take so long not because the fix is hard, but because finding the right fix is. Most engineers spend 20 to 60 minutes just understanding what’s wrong before they can act, not fixing anything, just trying to see the full picture. The framework that changes this has four steps: Orient, Isolate, Hypothesize, and Verify, and the order matters more than the tools.