Operations | Monitoring | ITSM | DevOps | Cloud

Adding Routing Intelligence To Your Observability Stack

Observability has a blind spot, and for most teams it sits at the network layer. You instrument your services, scrape metrics into Prometheus, ship logs somewhere searchable, and build dashboards that tell you when something is wrong inside your infrastructure. But the routing that carries traffic to and from that infrastructure often lives entirely outside the stack, watched through separate tools that do not talk to your alerting. This piece looks at why routing belongs in your observability pipeline and what it takes to get it there.

How Ecommerce Teams Can Scale Local Delivery Without Adding More Dispatch Staff

A store running 20 or 30 local deliveries a day can plan them by hand. Someone pastes the addresses into a maps app, eyeballs a sensible order, screenshots the list to each driver, and the day works out fine. Then the store grows. Ninety orders come in, a third of them carry a two hour window, three are marked urgent, and the two drivers are on different shifts. The same process now eats the whole morning, and the errors it produces cost more than the time it takes.

AI's Role in Enhancing Digital Commerce Operations

Artificial intelligence is quickly becoming a must-have for digital businesses, not just a nice-to-have. For companies looking to sharpen their operations, AI offers powerful ways to predict what's next, smooth out customer interactions, and keep transactions safe. It's not about replacing people, but giving them better tools. This lets teams focus on big-picture strategy while AI crunches data and automates tasks. This shift is changing what's possible in terms of how efficient a business can be, how happy its customers are, and how much it can grow.

5 ways agentic AI in ITOps will close the gap between alerts and action

Agentic AI in ITOps has emerged as a practical way to go beyond just detecting incidents. Modern IT teams have invested heavily in observability, yet the gap between detecting an issue and resolving it continues to widen. Three major challenges are driving this shift: This is where agentic AI makes a difference.

H1 2026 Cloud and SaaS Reliability Report

The first half of 2026 reinforced a key idea about Cloud and SaaS reliability - dependency risk. IncidentHub tracked 30,246 outages across 1,082 providers between January and June 2026. May was the busiest month, with 6,070 incidents. Cloud providers led in the total number of outages (4,723), followed closely by developer tools (4,589).

Custom deployment permissions for your environments (Beta)

You shouldn’t have to grant full repository admin rights just to let an engineer or release manager trigger a deployment. To solve the all-or-nothing access problem, Bitbucket Pipelines introduces custom deployment permissions (Beta). You can now gate specific environments to authorized users and groups, ensuring safer releases and easier compliance. Left: Add users and groups to gate deployments for this environment.

Use OpenTelemetry-native observability with Datadog from ingestion to investigation

The recent announcement that OpenTelemetry (OTel) has achieved CNCF graduation further reinforces OTel’s credibility as the industry standard for vendor-neutral telemetry. As organizations increasingly adopt the OpenTelemetry Protocol (OTLP), the OTel Collector, and OTel SDKs, they need an observability platform that supports OTel-native data without sacrificing flexibility or portability.

Software Release Management: A Practical Guide for Engineering Teams | Harness Blog

Software release management moves code through testing, approval, and into production with a clear rollback plan. 72% of organizations have hit a production incident from AI-generated code; developers now ship 63% faster (Harness, 2025). Effective release management needs defined stages, approval gates, automated testing, and the ability to roll back quickly. Feature flags, automated safety gates, and progressive delivery let teams ship faster and safer as AI raises code volume.

MongoDB Query Tracing in .NET with Sentry + OTLP

If your.NET app talks to MongoDB, you almost certainly want to be able to measure DB performance so that you can effectively debug any performance issues you might run into. For that, you need to know which database command was running, how long it took, and whether this was a one-off blip or part of a broader pattern. Ideally, you also want to pivot from that trace to related errors and replays without stitching the story together by hand.

Best PR Review Tools for AI-Generated Code (2026)

Most PR review tools answer one question: does this code look correct? They scan the diff, flag known patterns, catch security violations, and post inline comments before merge, which is useful, but only half the review. The other half is behavioral: does the code actually behave correctly once it’s running against your live system?