Build or Buy an AI IT Agent? Five Things Every CIO Should Consider Before Making the Decision
If you've discussed AI with your leadership team recently, chances are someone has asked the question.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
Why Most AI Pilots Never Reach Production
Most AI initiatives never make it out of the pilot stage. Gartner has forecast that 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, undone by poor data quality, weak controls, unclear business value, and escalating cost. The problem predates the current wave of generative tools. RAND's study of experienced practitioners found that more than 80% of AI projects fail, roughly twice the rate of IT projects that carry no AI component.
How Agentic AI speeds up troubleshooting application issues
One night, Daniel Rizzy was the only person awake on Zylker’s IT team, and the clock was already running. He was also the only thing standing between a P1 outage and 10,000 customers. Rizzy works nights for ZylkerXchange, Zylker’s foreign currency exchange app. He lives on the city’s outskirts, where the air is clean and quiet, and the night shift suited that life. Most nights, nothing happened. Some nights, everything did.
The Future of Digital Experience in Companies: What Changes with DEX, AI, and the Employee at the Center
For decades, companies measured IT efficiency through technical indicators: servers up, systems online, equipment working. But does that actually mean a good experience for the people doing the work?
Sentry for Agent Tracing
At any given moment, your AI systems can be down, slow, overloaded, or just returning bad results. Someone's gotta babysit the bots. Sentry traces across your AI pipeline, from user request to final response, so you can see exactly what's happening and fix it.
Waiting Is Expensive for AI Agents
Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.
Fix flaky tests with AI, and track future test work in Jira
In January we launched Tests in Bitbucket Pipelines – a single place to track, organize, and understand your test health over time. In April we added automatic flaky test detection so unreliable tests get flagged before they slow your team down. But spotting a problem is only half the battle. Day to day, your team still needs to act on a test – track it as work, clean it up, or route it to the right person.
LogicMonitor and Edwin AI: Autonomous IT for Hybrid IT Environments
Autonomous IT starts now with LogicMonitor and Edwin AI, built to help IT teams monitor complex hybrid IT environments, discover root cause faster, reduce downtime, and prevent incidents before they impact revenue or brand reputation. See how LogicMonitor brings AI-powered IT operations, observability, and incident prevention together for modern infrastructure teams.
How AI Agents Are Changing Each Agile SDLC Phase
The Agile software development lifecycle was designed to surface problems early, with short sprints, iterative testing, and continuous integration built on the premise that faster feedback loops produce better software. AI coding tools have changed the velocity equation across every phase of that loop, but the phases designed to catch failures are struggling to keep up because build speed and validation capacity have not accelerated at the same rate, and the gap between them is widening with every sprint.
AI Orchestrations
AI Orchestrations analyzes historical event and incident data, including past responder behavior, and surfaces actionable recommendations for event orchestration rules that reduce noise, improve triage, and lower MTTR — so teams can spend more time innovating and less time fighting fires.#AIOps.