Operations | Monitoring | ITSM | DevOps | Cloud

At 2 am, your migration goes live. By 2:07, error rates spike, and rollback isn’t an option. Cloud migrations, API rewrites, and architecture transformations rarely fail because of bad code. They fail because of how that code is released. Most teams still rely on a “big bang” cutover where infrastructure, services, and user-facing changes go live at once. This concentrates risk into a single moment.

If your Terraform install is insecure or inconsistent, it can quickly slow down your delivery. A single compromised file or a misconfigured backend can stop deployments for many services. Teams that set up Terraform correctly from the start can scale easily and avoid compliance issues.

Businesses today run on computers, cloud systems, and digital tools. One big failure can stop everything. A cyber attack, a power outage, or a software glitch can shut down operations for hours or days. Disaster recovery testing is how you prove you can restore critical services when the unexpected happens. 
 In 2026, with hybrid and multi-cloud estates, distributed data, and tighter oversight, this is not a once-a-year fire drill.

Good docs are how developers work with a product, from first look to daily use. That’s been true for a long time, and it’s becoming more true as developers increasingly hand that work to agents on their behalf. During the last quarter, we’ve been migrating docs.kosli.com from a static Hugo site to Mintlify, and now it’s finally live. Early reactions from our customers: “A marked improvement over the old docs in layout and usability.” “Looking sharp!”

Every minute counts when production systems go down. Yet the average enterprise NOC team receives over 1,000 alerts per day, according to a 2025 study by OpsRamp. Of those, fewer than 5% require human intervention. The rest? They are noise — redundant, low-priority, or symptomatic signals that bury the genuine incidents demanding immediate attention.

Based on insights from The Intelligent Enterprise podcast, “The Evolution from Automation to Autonomy” Every couple of weeks, The Intelligent Enterprise podcast steps away from the day-to-day noise of enterprise life to explore big ideas from a fresh perspective. In one recent episode, the focus turned to a question many organizations are still grappling with: What does it really take to build an AI-powered enterprise that works with people, not against them?

Engineering organizations rely heavily on developer feedback to improve internal platforms, tooling, and processes. However, that feedback is often scattered across disconnected systems such as external forms, spreadsheets, chat threads, and documentation tools. Because these systems are separate from operational data, teams struggle to correlate developer sentiment with measurable performance or reliability outcomes.

Meta's internal token leaderboard sparked a frenzy — and a reckoning. Tokenmaxxing without attribution is just cloud waste 2.0. Companies like Hudl and Duolingo use cost intelligence to connect every AI dollar to a business outcome.

You probably have Datadog. Maybe New Relic, maybe Dynatrace. Your observability stack has been solid for years — and you're still flying blind on AI cost. Here's why LLM observability needs a fourth pillar most tools skip, and how to build one that actually tells you what your models are costing you per request, per feature, per customer.

DNS Spy now goes well beyond DNS record monitoring. We've shipped SSL certificate discovery and security auditing, expanded the Security Center to 40+ automated checks across six categories, and built expiration tracking for both domains and SSL certificates — with tiered alerts so nothing expires without warning.