Systems | Development | Analytics | API | Testing

8 Best AI SRE Tools for Platform Engineers

A Kubernetes deployment succeeds, but the application starts returning errors. CPU utilization looks normal, yet response times climb. An autoscaler adds capacity without improving performance, and the on-call engineer spends the next hour moving between dashboards, deployment histories, logs, and configuration files. The difficulty is rarely a lack of monitoring data. It is determining which signals matter, how they relate, and what action will restore service without introducing another failure.

The Future of the Digital Front Door: Trends Shaping Healthcare Access in 2026

‍ A digital front door (DFD) connects patient-facing channels with clinical, administrative, and financial workflows. It supports appointment scheduling, registration, care navigation, and payments through integrated websites, applications, and messaging. In 2026, digital front door healthcare trends increasingly center on automating these connected workflows. Platforms such as Luma and Notable combine patient interactions with operational processes and electronic health record integration.

The Next Question Matters More Than the First Answer

With Claude Dashboards entering beta, Anthropic is making it possible for people to ask quick, exploratory questions about their business data and get a dashboard back. You don't need to write SQL or build a visualization. I think that's a good thing. We've spent years trying to make analytics more accessible, and anything that shortens the path from question to answer is progress. I'm increasingly asked what the future of business intelligence looks like when AI can do more of the analysis for us.

Agentic AI Security and Governance: Risks, Controls, and a Practical Checklist

Agentic AI security is the set of controls that keep AI agents from accessing, changing, or sharing more than they should. It covers who an agent acts for, which data and tools it can use, which actions need approval, and how its activity is recorded and reviewed. AI agents are moving from pilots into daily operations. They read invoices, update records, answer customers, and trigger workflows across business systems.

LLM and AI Agent Observability: What to Monitor, Evaluate, and Test

LLM observability is the practice of collecting and analyzing data about how a language model application or AI agent behaves, including prompts, responses, tool calls, cost, speed, and errors, so teams can find problems, measure quality, and improve results. AI agents can now plan tasks, call tools, and work across business systems. That makes them powerful, and it also means a single request can involve many steps that no one sees directly.

14 Best API Mocking Tools In 2026 (Open Source And Paid)

Your frontend is ready, but the backend endpoint it depends on ships next sprint. Your QA team wants to test a payment flow, but the third party sandbox is down again. Your CI pipeline fails twice a week because a staging database returned different data. All three problems have the same fix: an API mocking tool. API mocking tools let you simulate an API, or any dependency your service talks to, so you can build and test without waiting on the real thing. The hard part is picking one.