Welcome to Hit Subscribe’s Monthly Digest! In this edition, we’re excited to share a collection of recent blog posts we’ve written for our clients. Plus, stick around till the end—we’ve included a meme of the month to keep things fun!
AI Agent Evaluation Explained: A Practical Guide
AI agents are now moving to production environments, but there’s a hidden crisis: they fail far more often than most teams realize. Research shows that even a tiny 1% error rate per step can compound into a 63% chance of complete failure by the hundredth step.
AI agents use tools, maintain memory, reason across multiple steps, and act on their own. That’s what makes them powerful, but also what makes them hard to evaluate.
This guide breaks down why standard LLM evaluation falls short, what proper AI agent evaluation looks like, which evaluation metrics and methods matter, and how to build a framework that catches failures before they reach users.
Explanation of Immutable Backup and Its Importance
Modern ransomware attacks often target backup repositories before encrypting production systems, eliminating an organization’s ability to recover.
At the same time, organizations are giving AI agents greater access to production environments, creating new risks around automated actions and credential misuse. While controls such as least-privilege access remain important, they cannot prevent every failure scenario.
When other defenses fail, immutable backups provide a critical layer of protection by ensuring recovery data cannot be altered or deleted.
This guide explains what immutable backups are, how they work, and why they have become a foundational part of modern cyber resilience strategies.
Does Google Workspace Back Up Your Data? A Complete Guide
Does Google Workspace back up your data? Not exactly. While Google is responsible for maintaining the infrastructure and availability of Google Workspace, organizations remain responsible for protecting their own data from accidental deletion, ransomware, insider threats, and other risks.
This guide explains Google’s shared responsibility model, what protections are included, where the gaps exist, and why many organizations choose to implement a dedicated Google Workspace backup strategy.
AI End-to-End Testing: What It Is and How It Works
AI end-to-end testing uses AI agents to interact with applications like real users, executing tests from natural language instructions rather than relying solely on scripted selectors and workflows.
By reducing the maintenance burden of traditional E2E tests, AI-powered testing helps teams create more resilient test coverage while allowing QA engineers to focus on strategy instead of test upkeep.
This guide explains how AI end-to-end testing works, its benefits and limitations, and how teams are using it to improve software quality.
iOS Mobile Testing: Everything You Need to Know to Get Started
iOS testing presents unique challenges, from device fragmentation and evolving iOS releases to strict App Store requirements and complex automation frameworks. Even well-tested applications can encounter issues that only appear on specific devices, configurations, or accessibility settings.
Effective iOS testing requires a balanced strategy that combines real-device testing, automation, manual exploration, accessibility validation, and thorough regression coverage before release.
This guide explores iOS mobile testing best practices, common challenges, and how modern AI-powered testing tools can help reduce maintenance overhead while improving test coverage.
Windows Application Testing: A Complete Guide
Windows application testing ensures desktop applications function correctly, perform reliably, and remain compatible across different Windows environments. Unlike web testing, Windows testing must account for varying UI frameworks, operating system behaviors, and installation requirements.
A successful testing strategy combines functional, UI, compatibility, performance, security, and regression testing while balancing manual and automated approaches.
This guide explains the fundamentals of Windows application testing, common challenges teams face, and best practices for building a scalable testing process.
Understanding the Patient Intake Process: A Complete Guide
For skilled nursing facilities, an efficient patient intake process is critical to maintaining occupancy and delivering timely care. When admissions are delayed by manual workflows or disconnected systems, beds remain empty longer and staff workloads increase.
By streamlining intake and admissions, facilities can reduce referral-to-admission delays, improve census levels, and lessen the administrative burden on care teams.
This guide explores common patient intake challenges and strategies for creating a faster, more efficient admissions process.
What Is Software Quality? A Beginner’s Guide
Software teams all want to build quality products, but “quality” can mean different things to different people. It may refer to reliability, performance, security, usability, or all of the above.
The challenge is that quality is difficult to improve when it isn’t clearly defined or measured. As applications grow and release cycles accelerate, teams without a structured approach often face more defects, higher costs, and reduced user trust.
This guide explains what software quality is, how teams measure it, and the practices that help maintain it at scale.
Data Warehouse Testing: A Comprehensive Guide
If you run data, AI, or analytics, here’s the bottom line: data warehouse testing is no longer optional—it’s the difference between trust decisions and expensive mistakes.
When your warehouse breaks quietly, your forecasts, your dashboards, and now your AI agents break with it.
This post is for data engineers, QA leads, and analytics teams who need a clear, practical playbook for data warehouse testing in 2026.
We’ll define the term, walk through the seven steps that matter, compare data warehouse testing to ETL testing, look at how agentic AI is reshaping the practice, and show you what good looks like with real customer outcomes from Tricentis Data Integrity.
What Is Data Validation? Methods and Examples
Every organization relies on data being trustworthy. However, inaccurate, incomplete, or inconsistent data tampers with analytics, leading to incorrect insights.
It also affects business outcomes and product decisions. The effects can lead to failed system migrations or risk compliance for your team.
Data validation is the technique or process that ensures clean and accurate data. As a result, it leads to better insights, findings, and decision-making.
In this post, you’ll learn how data validation works, why it is essential, and how to implement it effectively. You’ll also explore related concepts like data cleaning and verification, as well as how modern automated agentic tools can be leveraged to transform this process.
Types of Software Testing and When to Use Them
Software testing isn’t a single activity. It’s a collection of testing methods designed to uncover different types of defects throughout the development lifecycle.
The challenge is knowing which tests to run, when to run them, and how they fit together. Relying too heavily on one testing type or skipping another can leave critical gaps that allow defects to reach production.
This guide explores the major types of software testing, when to use them, and how they work together to support a comprehensive quality strategy.
Defect Density in Software Testing: A Complete Guide
Defect density is a software quality metric that measures how many defects exist relative to the size of a codebase. It helps teams identify high-risk areas, track quality trends over time, and make more informed testing decisions.
While often overlooked, defect density provides a quantitative way to assess software quality and uncover potential problem areas before they impact users.
This guide explains what defect density is, how to calculate it, why it matters, and how modern testing approaches can help reduce it.
Build Automation: What It Is and How It Works
If you’ve ever watched a developer manually compile code, run tests, and package a release, you know exactly why build automation exists. Manually building software can be slow, error-prone, and scales terribly.
Build automation fixes all of that. It turns the chaotic, manual process of shipping a product from source code into a fast, repeatable, and reliable workflow. Modern software teams depend on it every single day.
This post will break down what build automation is, how it works inside CI/CD pipelines, which tools teams rely on, and how you can get started. We’ll also cover best practices, common pitfalls, and how emerging agentic AI technology is starting to reshape the space.
Meme Of The Month
Straight from our internal Slack channel—because memes are fun, and so are we.
That’s All, Folks!
Thanks for catching up with us and we’ll see you next month. In the meantime, feel free to reach out if you have any questions, want to share your thoughts, or want to talk shop!
