Operations | Monitoring | ITSM | DevOps | Cloud

Debug Live Production Apps in Codex with Lightrun MCP

Lightrun’s Dan Putman demonstrates the power of the latest Lightrun MCP skill. Watch how your AI code agent can now debug live applications directly in production. By connecting OpenAI's Codex to real-time runtime data via the Lightrun MCP, engineers can now generate and validate hypotheses using live telemetry and snapshots, without breaking flow. Ready to bring runtime context to your AI agents?

90% AI Adoption. Still Failing. DORA Explains Why.

AI adoption is nearly universal. So why are most teams still struggling? In this session from GitKon, Nathen Harvey, head of DORA at Google Cloud, shares findings from the 2025 DORA State of AI-Assisted Software Development report, drawing on data from nearly 5,000 developers worldwide. The answer isn't more AI. It's what surrounds it.

That's Not a Job for an LLM: The Right Way to Apply AI to Network Operations

LLMs have sucked all the oxygen out of the AI conversation — but AI is much more than just LLMs, and network engineers have been using AI techniques (machine learning, statistics, fuzzy logic, expert systems, neural networks) for decades. So what should LLMs be doing in network operations, what shouldn't they be doing, and how do agentic AI architectures fit in?

What is AI SRE? The Complete Guide to AI-Assisted Site Reliability Engineering

It's 2:47 AM. PagerDuty fires. You open a Slack alert and see: p99 latency spike on checkout-service. You SSH into the host, check dashboards in four tabs, grep logs for the last 20 minutes, and eventually find a slow query introduced in a deploy six hours ago. It took 34 minutes. You resolved it, w Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Code Agents Need Observability

For those of us using tools like Claude Code, Codex, or Gemini, we already know they’re powerful. They can write code, refactor functions, open PRs, even run commands. For a lot of developers, they’re already part of the daily workflow. But once you zoom out beyond the individual developer, the biggest problem isn’t productivity. It’s control. AI coding tools are powerful, but they introduce a new, unpredictable cost layer that most teams don’t fully understand.

How AI Is Reshaping Bill of Materials Management

Most of what gets written about AI in manufacturing is hype. I've sat through enough vendor demos to recognize the pattern: a slick interface, cherry-picked examples, and a vague promise that machine learning will "transform" something. Half the time the underlying problem could have been solved with a structured database and a junior analyst.