Operations | Monitoring | ITSM | DevOps | Cloud

PagerDuty’s new intelligent Model Context Protocol (MCP) tools, recently announced in Early Access, are a step toward autonomous operations. Here’s how they help customers get there.

A platform engineering team can design robust Golden Paths for agent use yet still be unprepared for what happens after adoption. An agent may authenticate properly, call the correct tools, adhere to approval gates, and complete tasks without incident, but new operational risks arise once multiple teams begin running agents continuously and in parallel. We’ve encountered these risks firsthand at Datadog.

Distributed AI training workloads impose complex scheduling requirements that Kubernetes’s built-in scheduler can’t meet. Kubernetes schedules pods individually and independently, but distributed training introduces two requirements that break this model: Pods must land on hardware with the right inter-GPU bandwidth, and all pods must be scheduled simultaneously. If either requirement goes unmet, training stalls or runs far below the hardware’s potential.

AI coding tools such as Cursor are becoming a significant source of engineering spend. But Cursor costs can be difficult for FinOps teams to manage. Cursor’s usage data alone doesn’t tell you how costs break down across users and models, and fixed-threshold alerts may not catch an unusual cost spike if spend remains below the threshold. Datadog Cloud Cost Management (CCM) brings Cursor costs into the same place where you monitor cloud, SaaS, and other AI spend.

Pipeline Intelligence already recommends processors, reads live telemetry, detects log types, and generates processor bundles from natural language. But most of it lives inside a single processor node. You still have to know which one to open and what to ask for. This changes today. Bindplane Agent is your AI assistant inside Bindplane, ready to act on what you describe in plain language.

Worldwide AI spending will reach $2.59 trillion in 2026, up 47% from 2025, according to Gartner. Yet only 37% of organizations report any earnings impact from AI, McKinsey finds. These AI statistics cover what companies spend, what AI costs to run, and the ROI they're actually getting. That gap between the two headline numbers is the story of AI in 2026.

Datadog has been named the Company to Beat for observability platforms in the August 2026 Gartner AI Vendor Race research. Datadog has also been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms for the sixth consecutive year. We believe that these recognitions reflect what we have been building toward for more than a decade: a single platform where teams can observe, secure, and act on everything that matters across their technology stack.

Picture two teams running the same million input tokens through the same model. One team’s tokens are cache hits, queued through the batch API. The other team’s are fresh, sent live. On a current-generation OpenAI model, cached input runs about a tenth the price of a fresh token, and batch processing cuts whatever’s left in half. Stack the two: at a list rate of $2 per million tokens, one team’s bill comes to 10 cents, the other’s to two dollars.

According to a 2025 report from Ravio, entry-level hiring (especially in engineering roles) has collapsed by more than 73% due to increasing AI capabilities. That means junior developer jobs are disappearing. At the same time, demand for senior engineers keeps climbing because organizations need more people to manage and optimize their complex AI agents.