Why AI coding tools need a new kind of engineering discipline, and what we’re doing about it A while back, a team of our senior engineers tried something that is now pretty common. They took a feature they would normally estimate at around 16 hours and built it with an AI coding agent. It took… Continue reading AI-Integrated Governed Engineering
Traditional application support keeps systems running. The real issue, though, is whether or not it ever improves them. The monthly service review looks good. In fact, it looks better than good. SLA compliance is above target, tickets are closing quickly, availability matches the contract. During the meeting, a business lead mentions an application that keeps… Continue reading Your Application Management Services (AMS) Provider Is Meeting Its SLAs. So Why Aren’t Your Problems Going Away?
INTRODUCTION Enterprise software testing is in the process of shifting from its fundamentals by incorporating Generative AI, Retrieval Augmented Generation (RAG), and intelligent knowledge systems. Traditional QA approaches of manual test design, automation, and defect management are no longer enough in today’s development pipeline, which requires speed, accuracy, and continuing validation. This blog provides an… Continue reading Building an Enterprise Agentic AI Testing Assistant – A Practical Implementation Guide
How we automated the verification of hundreds of dashboards, alerts, and SLOs during a New Relic to Grafana LGTM migration. By Mahesh Shekharapp The Validation Gap Nobody Budgets For Your team has just moved its observability stack off New Relic and onto Grafana’s LGTM stack (Loki, Grafana, Tempo, Mimir). Dashboards, alert policies, SLOs (Service Level… Continue reading Validating an Observability Migration at Scale with Agentic AI
Stop adding AI. Start reshaping the experience. For thirty years, enterprise software has run on one bargain. The user learns the system and does the work by hand, task after task, screen after screen. AI breaks that bargain. When software can read what you want, write its own answer, act for you, and get sharper… Continue reading AI UX Design for Enterprise Applications: A Practical Framework
How modern ETL validation framework are redefining data confidence, making pipelines trustworthy and auditable, and quick enough that teams actually bother running them. Envision the following situation: your team has been working for weeks to move a vital database from SQL Server to Databricks, involving millions of rows and various transformations, yet the date set… Continue reading ETL Validation Framework: How to Cut Data Reconciliation Time by 67% (Without Writing Python)
What is the gap between AI adoption and AI value? McKinsey’s 2025 State of AI survey found a clear gap between adopting AI and generating measurable business value from it. While 88 percent of respondents said their organizations use AI in at least one business function, only about 6 percent qualified as high performers. McKinsey… Continue reading Microsoft 365 E7 Explained: What It Bundles, and Why Licensing Alone Won’t Close the AI Value Gap
A dashboard in Tableau appears simple when viewed in a browser. Before becoming visible to the end-user, many things happen in the background. In an enterprise-level environment, dashboard data comes from various sources, including ERP systems, CRM systems, databases, cloud applications, and APIs. The data passes through ETL/ELT processes to be ready for reporting. Tableau… Continue reading Tableau Server Architecture Explained: How Enterprise Dashboards Really Work
Data quality and report quality get treated as the same problem more often than not, and that’s usually where trouble starts. A dashboard can be built on flawless data and still mislead someone looking at it, or it can be visually polished while quietly reporting the wrong numbers underneath. Telling those two failure modes apart,… Continue reading Power BI Testing Guide: From Data Validation to Report Accuracy
Over the last few years, I have worked on many AI solutions across different domains such as health care, HR, project management, customer support, and finance. One thing I have noticed is that when people think about AI, they immediately think about a chatbot. Many teams start with the question “How can we add a… Continue reading AI UX Patterns: When to use Chat vs Dashboard vs Automation