Operations | Monitoring | ITSM | DevOps | Cloud

6 use cases for agentic AI in major IT incident management

Enterprise IT operations leaders are realizing that legacy incident management processes cannot keep pace with today’s sprawling, hybrid-cloud enterprise environments. Enterprise IT doesn’t look anything like it did even five years ago. Hybrid cloud architectures, distributed microservices, and increasingly rapid CI/CD cycles have increased the speed and complexity of IT operations by orders of magnitude, leaving ITOps teams struggling to keep up.

What Is an MCP Server for Infrastructure? How AI Agents Deploy Safely

An MCP server is the standardized bridge that lets AI agents like Claude Code and Cursor operate real infrastructure - deploy apps, provision databases, manage environments - through one governed API. Here's how MCP servers work for infrastructure, why they matter, and how to give agents production access without losing control. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

How AI Shopping Assistants Are Turning E-Commerce Search Into an Operational Advantage

Conversational AI in retail crossed into production faster than most technology adoption cycles typically allow. What started as a novelty chat widget is now treated by operations and product teams as a core piece of the customer-facing stack, the case for that reclassification rests entirely on operational outcomes rather than interface aesthetics.

Inside the Buyer's Decision: Governance, Trust, and Production-Ready Agentic AI

Why do so many AI pilots succeed in testing but fail to reach production? In this webinar, Resolve and IT leaders from RisePoint explore one of the biggest challenges facing enterprise AI adoption today: trust. While organizations are investing heavily in AI agents and automation, many initiatives stall before deployment due to governance concerns, compliance requirements, risk management, and lack of operational visibility.

What is an AI software factory?

Ask a software engineer what they do and the answer, for years, has been some version of "I write code." That assumption is unwinding fast. AI agents can now write code, review pull requests, run tests, and ship to production, and they're taking on a fast-growing share of that work. As agents absorb more of the execution, the human role shifts.

Base44 vs Lovable: Which AI App Builder Should You Use in 2026?

Base44 vs Lovable compared: features, backend, pricing, popularity, and which AI app builder fits your use case - plus how to take either prototype to production on infrastructure you control. Melanie leads content at Qovery. She covers platform engineering trends, Kubernetes operations, FinOps, and the tools that help engineering teams ship faster.

The New Software Creator: Why AI Changes the Governance Problem, Not Just the Speed Problem

The conversation about AI and software development has mostly been about velocity. Developers write code faster. Pull requests ship sooner. Backlogs shrink. That part is real, and it matters. But there's a bigger shift happening underneath it, and most engineering leaders I talk to are only just starting to feel its weight. AI hasn't just made developers faster. It has fundamentally expanded who can create and ship software. That changes things in ways that velocity metrics don't capture.