Systems | Development | Analytics | API | Testing

Gone are the days when core banking teams relied solely on long-winded manual test cycles, midnight war rooms and crossed fingers before a go-live. Today, the industry stands at the edge of a seismic shift, driven by the power of test automation. Having worked extensively in the complex and highly regulated world of core banking systems, we’ve seen this transformation unfold firsthand.

The race to operationalize private AI at enterprise scale isn’t just about models and algorithms—it’s about infrastructure that refuses to compromise. Welcome to the inaugural post of AI in a Box, a three-part blog series that unpacks how Cloudera, Dell Technologies, and NVIDIA are redefining enterprise AI with a turnkey solution that unifies cutting-edge AI-optimized hardware, intelligent data orchestration and AIOps tooling, and zero-trust governance.

As enterprises increasingly embed AI assistants into their core workflows, trust becomes the currency of adoption. Salesforce Einstein Copilot is fast becoming a central productivity layer across CRM, Sales, and Service modules. But with great potential comes greater responsibility, especially for quality assurance teams. Validating the trustworthiness of AI outputs, guarding data privacy, and ensuring reliable decision boundaries are now non-negotiable in enterprise environments.

The world of artificial intelligence is undergoing a seismic shift, with the emergence of agentic AI redefining the landscape of API development and management. As businesses and developers navigate the complexities of digital transformation, understanding the implications of this groundbreaking technology becomes paramount.

This post will walk through the creation of a realtime chat application with Next.js and deploying it to Netlify.

Based on a sample of data from ~1,000 agencies and 14,000 clients, we estimate that agencies lose about 38% of their clients every year. Based on my 1,000s of conversations with agencies over the years, I think I know one of the big reasons why. Most agencies have been stuck in the same pattern for years: Do good work, report on it, wait for feedback, hope for renewal. They start strong—engaged with the client’s leadership, aligned on strategy and goals, excited to build.

Earlier this year, SmartBear acquired QMetry, a move that’s more than just a strategic acquisition. This move reflects a deeper commitment to enterprise customers – helping teams: As Product leader of Test Management at SmartBear, I’ve had the opportunity to help define our roadmap. Our vision for QA teams is clear: empower them to deliver better quality and build confidence – and consistently hit release deadlines.

Peanut butter has jam. Wine has cheese. Great Britain has pubs. The world is filled with things that are great alone, but that together, create an even greater – dare I say, legendary – combination. Now it comes to the world of data platform management. Sorry, I’m getting ahead of myself. You see, a few months back I predicted 2025 was going to be a big year for data platforms. One such solution being Hitachi Virtual Storage Platform One (VSP One).

As software systems get increasingly complex every day, the challenges of effective testing also escalate. Software testing involves handling large datasets, complex workflows, and shorter release cycles.

We have all seen the frenzy of Devin AI—teams racing to spin up models, orchestrate data flows, and automate every possible touchpoint. But beyond the hype, two architectural patterns quietly power next-generation pipelines: Model Context Protocol (MCP) and Agent-to-Agent (A2A). Below is the roadmap for our deep dive into MCP (Model Context Protocol) and A2A (Agent-to-Agent).