Operations | Monitoring | ITSM | DevOps | Cloud

“Autonomous” doesn’t mean uncontrolled. AI-driven IT in life sciences needs guardrails based on GxP, risk, and validation. Low-risk systems can automate; high-risk systems need human approval and documentation.

Claude Mythos may be wrapped in hype, but the core signal is real: AI is making vulnerability discovery much faster, which means defenders have less time than ever to patch and enforce secure configurations. The real risk isn’t just smarter models, it’s that security teams will face a flood of new findings while the window between disclosure and exploitation keeps shrinking.

AI observability covers performance, quality, reliability, safety, and cost. Most tools handle the first four. Here's what each pillar means, which tools cover which, and why cost is the dimension enterprises keep missing.

Windsurf pricing in 2026: Pro is $20/mo, Max is $200/mo, Teams $40/user. Full plan comparison with Cursor, Copilot, and Claude Code.

The IT service management (ITSM) industry stands at a real inflection point. For decades, service desks have operated on a fundamentally reactive model — employees face problems, submit tickets and wait for human analysts to diagnose, triage and resolve their issues. Automation improved throughput within that model, but it never challenged the model itself.

Your dashboards are green. Your thresholds are calm. Then a cascade failure starts and you don't know until users flood your status page. Traditional monitoring is reactive by design. Anomaly detection in observability changes that equation entirely.

Last month, we introduced Agentic Pipelines, a new way to orchestrate AI agents to automatically, and routinely, handle the repetitive engineering chores so you can get back to solving the fun, cool problems. When we launched, Agentic Pipelines supported Atlassian’s developer AI agent, Rovo Dev. Today, we’re opening up Agentic Pipelines to even more teams: You can now run agentic steps in your pipeline with Claude as the provider.

The average person would need 180 million years to read everything published online in a single day. For organizations trying to track what people say about their brand, manual monitoring stopped being viable somewhere around 2015. AI-powered media monitoring tools now process this impossible volume automatically, detecting brand mentions, analyzing sentiment, and flagging potential crises before they spiral.

Last week, we introduced Agent Timeline, a powerful new observability experience purpose-built for debugging AI agent workflows in production. Agent Timeline uniquely connects AI-layer visibility to full-stack observability by organizing telemetry around an agentic conversation. A conversation contains one or more agent executions, each of which may contain LLM calls, tool invocations, handoffs, retries, human escalations, and downstream system calls.