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Building AI agents that work reliably in production requires solving problems that have nothing to do with AI. While teams focus on prompt engineering, model selection, and agent orchestration, a different class of challenges emerges at deployment. These have little to do with LLMs and everything to do with keeping agents and clients synchronized in realtime. Over the past few months, we've spoken with engineers at over 40 companies building AI assistants, copilots, and agentic workflows.

AI has completely reshaped the boundary between human imagination and what’s possible. Along the way, AI use in business has become mainstream, with software delivery among its top adoption areas. In 2026, leading global technology companies are now using AI to generate the majority of their code, with some development teams reporting that they haven’t written code manually in months.

Thanks to AI, software today ships faster and with more complexity than ever before, and performance teams that rely on workflows built for a slower era are at risk of falling behind. Reliance on manual steps, niche expertise, and disconnected tools create bottlenecks that add risk to every release. Tricentis NeoLoad is leading this paradigm shift with AI-powered performance capabilities that close the gap and match the pace of validation to that of modern software delivery.

In 2025, insightsoftware was recognized in Gartner’s Magic Quadrant for Financial Planning Software. The recognition focuses on insightsoftware’s JustPerform product, which offers web-based budgeting, planning, and forecasting with an Excel-like interface and self-service reporting, dashboards, and analytics.

Astera Centerprise is already one of the most AI-forward data platforms available. Its built-in agentic AI creates data models, builds ETL/ELT pipelines, generates source-to-target mappings, orchestrates workflows, prepares data, and deploys schemas to production, all through natural language. You describe what you need; the AI uses real Centerprise tools to build it.

Data infrastructure growth has a direct, measurable relationship with energy consumption. As organizations ingest more events, retain more data, and deploy more always-on services, infrastructure energy use increases—often faster than business value. For streaming systems, this effect can be amplified by long-running clusters, peak-based sizing, and duplicated pipelines. Sustainability in this context is not about environmental reporting or corporate commitments.

Whether you’re building your first iOS app or improving user engagement, understanding push notifications is essential. They’re how apps stay connected with users, delivering updates, reminders and alerts even when the app itself isn’t running. In this guide, you’ll learn how to implement iOS push notifications from setup to debugging, using Swift and Apple Push Notification Service (APNs) in a real app.

The AI coding and Agent Harness approach is no longer experimental. This is likely the most impactful agentic AI use case in production today, and Claude Code is one of the solutions really leading the charge. But as engineering teams race to adopt Claude Code across their organizations, a critical question emerges: who's governing all that LLM traffic?

The recent article on OpenAI overhauling its payment systems to reduce its dependency on Stripe highlights an important tension many digital platform builders face today: how to balance usage-based monetization with the realities of payments infrastructure dependency.