Operations | Monitoring | ITSM | DevOps | Cloud

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.

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 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 platform has been live for six months. Workflows are running, the virtual agent is fielding requests, and the vendor dashboard shows deflection numbers are going up. Then someone pulls the actual ticket volume report, and it looks almost identical to the one before the rollout. This comes up constantly in enterprise IT, and most teams respond the same way. They tell themselves the platform needs more automations, a wider user base, and another quarter to mature. Months pass.

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.

Author: Matthes Derdack Businesses rely on countless systems, applications, and services to operate without disruptions. Whether it is cloud infrastructure, manufacturing equipment, IoT devices, healthcare platforms, or enterprise applications, every second of downtime can impact revenue, customer trust, and operational efficiency.

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.

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

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.

When a PostgreSQL query runs slowly, engineers often start with EXPLAIN ANALYZE. The output is a tree of plan nodes, each one describing a step the database took to execute it. A query with several joins and a subquery can produce 20 or more nodes. But the plan gives no visual indication of which node corresponds to each clause in the SQL text. Diagnosing the problem means viewing the plan in one window and the query in another, manually tracing connections between them.