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Ivanti Agentic AI for ITSM
Meet your digital teammate. Persona-based AI agent designed for critical ITSM workflows. Transform IT operations with AI agents that plan, coordinate, and execute autonomously, delivering measurable business impact through intelligent automation. Your conversational front door to IT that replaces forms with natural language, cutting ticket load and improving data quality through guided capture.
Women In Tech Panel - Engineering with AI in the Stack
Every team is doing something with AI right now. What that something is, is an entirely different question. And whether that something is successful? Most teams are still figuring it out as they go.
The AI Engineering Playbook: How to Evaluate & Iterate at Every Phase of Development
AI coding tools are accelerating development velocity, creating a release challenge most teams aren’t equipped for. Without controlled rollout, higher change velocity makes it harder to know which specific release drove the results you’re seeing in production. And when teams use AI, to build AI – LLM apps and AI agents– complexity multiplies. Traditional observability can’t ensure AI agent quality, performance, and cost-efficiency at production scale.
How to collect telemetry from Claude Code and Codex
#aicoding #softwareengineering #claudecode
Seedance 2.5: Cinematic AI Storytelling
In the rapidly expanding digital economy, the ability to produce high-quality video content at scale has become the primary competitive advantage for e-commerce brands, self-media creators, and digital production studios. As audience attention spans continue to shrink, the necessity for high-fidelity, emotionally resonant, and visually consistent video content has reached an all-time high. This is where Seedance 2.5 enters the picture, representing a significant leap forward in generative AI video technology.
Overview of AI Evaluation (The Context Window #05)
Can you actually trust an AI agent? In this pre-recorded episode of The Context Window, Nicole van der Hoeven sits down with Yas Ekinci, an engineer on the Grafana AI team, to talk about evals — how Grafana measures the quality and reliability of the AI it ships. They get into the difference between online and offline evals, why reviewing AI-generated code has become the real bottleneck, the "final answer problem" of plausible-but-wrong outputs, and o11y-bench, Grafana's open benchmark for observability agents. Along the way.
How AI-First Operations Unlocks Compounding Engineering Productivity
Engineering teams have plenty of ideas, but they’re often short on time to act on them. As software systems grow more complex, an increasing share of engineering capacity is consumed by non-building activities: investigating alerts, coordinating fixes, and managing operational incidents. Every hour spent diagnosing failures is an hour not spent shipping features or experimenting with new product ideas. Over time, that lost capacity compounds.
Why correlation is no longer sufficient in an AI-driven operating model?
If your incident response still depends on engineers stitching alerts together… You don’t have observability. You just have dashboards. Correlation has to be automatic, contextual, and system-aware, or it becomes expensive noise.
Creating an agentic feedback loop with reliability guardrails
Reliability guardrails help make sure that your applications stay reliable without slowing down. In an earlier blog, we went into why agentic AI development needs reliability guardrails. It went over how the increased speed of AI development demands automated guardrails to verify resilience and what kinds of tests these guardrails should cover. But that’s only the beginning. By themselves, guardrails act as a gate to ensure resilience mechanisms hold under rapid changes.