AI Works in Demos. But Can It Survive Production?
AI looks great in a demo. The real test is production. In this week's Zero Ticket Minute, Ian explains why success isn't about what AI can do. It's about what it can reliably resolve.
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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.
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.
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.
The secret behind Carnegie's fortune and the lesson for the AI era
Point A: 1835. Andrew Carnegie is born in a weaver’s cottage in Dunfermline, Scotland. The cottage has one main room, which the Carnegies share with another family. Point B: 1901. Andrew Carnegie becomes the richest man in the world when Carnegie Steel Company wins the Iron vs. Steel industrialists’ war, and he sells the company to J.P. Morgan for the modern equivalent of $450 billion.