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‍ A digital front door platform connects patient-facing services with clinical and administrative workflows. Capabilities can include appointment scheduling, digital intake, patient communication, insurance verification, and payments. Coverage varies by vendor, product configuration, and supported electronic health record (EHR) integrations. Evaluating a digital front door (DFD) solution should cover complete workflows, integration depth, usability, security, and total operating costs.

‍ A digital front door strategy coordinates patient access, engagement, and supporting health system workflows. It connects provider search, scheduling, registration, payments, and follow-up through integrated digital services. Effective digital front door (DFD) implementation requires EHR integration, accessible interfaces, secure data exchange, and clear operational ownership.

Your manual test library is more than documentation. It contains years of decisions about what your product should do, and how to tell when it doesn't. That knowledge is a valuable starting point for automation, not something you should have to recreate from scratch. The clearest example came up in a customer conversation. The head of quality at a fast-growing fintech told me his company pays an outside firm to run its manual regression, with a bill for every test executed.

AI coding tool spend is projected to top $13 billion in 2026, compounding at more than 60% a year. These agents don't just autocomplete a line of code anymore. They plan. They act. They call tools, run commands, and touch other systems on your behalf. This is pretty different from the assistant that used to live in your IDE sidebar. And that's exactly where the risk lives. An AI coding agent is software that writes, tests, and ships code independently. This level of autonomy earlier tools never had.

That's a wrap on API + AI Summit 2026, Kong's biggest in-person event yet. The event brought together more than 1,000 engineers, architects, and leaders in Los Angeles to discuss running AI systems in production, tackle shared architecture challenges, and swap real-world insights. Recordings from this year’s event will be available in the coming weeks..

shoulda-matchers gives you one-line RSpec and Minitest matchers for common Rails behavior — it { should validate_presence_of(:name) } replaces a hand-written validation spec. Add gem "shoulda-matchers", "~> 8.0" to your Gemfile’s test group, then add the configuration block to spec/rails_helper.rb naming your test framework and library. Version 8 works with Rails 8.1 and rspec-rails 8.

In a release test I published in July, Katalon’s AI runner reported seven cases passed and four failed. I could open the steps, inspect their screenshots, and watch the browser session. The failed rows showed which expectations broke. The passed rows deserve the same scrutiny: what had the agent observed before it called each step passed? An AI test runner has to perform the action and judge the outcome. A false pass is a passed verdict when the expected behavior did not occur.

Your team has more changes to test, more environments to cover and less time to maintain regression suites. AI can help produce tests, but that introduces another workload: deciding whether those tests check the right behavior, whether a repair hides a defect and whether the evidence is enough to support a release. Agentic QA tools aim to take on more of that work, from planning and authoring to execution and failure investigation.

There's a conversation happening in a lot of data teams right now. It goes something like this: the lakehouse is clearly where things are heading, Apache Iceberg is becoming the format everyone wants to land in, and the AI use cases the business is asking for need clean, open, queryable data.