Systems | Development | Analytics | API | Testing

Key Differences Between an LLM and a True AI Agent

What is an AI Agent? In under 30 seconds, Marcus Merrell, co-host of Test Case Scenario, breaks down the key difference between a basic LLM (like ChatGPT) and a true AI agent. Hint: one gives you answers — the other takes action. Subscribe for more insights on software testing, AI, and quality engineering from the experts at Sauce Labs.#AI.

9 Mobile App Testing Strategies for 2025

The goal of any mobile product is to create an app experience that’s innovative and new. But you must accomplish specific, necessary steps between crafting a clear vision for your app and creating a mobile application which is where a mobile app testing strategy comes in. Today, mobile app testing encompasses a vast array of coverage areas, including functional testing, usability testing, performance testing, security testing, compatibility testing, and more.

Maximizing the Power of AI for ISVs: From Dashboards to Predictive Intelligence

Artificial Intelligence (AI) is no longer a futuristic ambition — it’s the strategic reality of today’s most competitive Independent Software Vendors (ISVs). In our recent Seamless Intelligence: Real-World AI Success Stories from Innovative ISVs webinar, I broke down how AI is transforming the ISV landscape, offering a blueprint for how organizations can move beyond dashboards, and into the age of predictive and generative intelligence.

Best Claude 3.5 Sonnet Style For Code: How It Improves Developer Workflows

As AI progresses to shape the future of software development, platforms such as Claude 3.5 Sonnet are making significant strides as programming powerhouses when it comes to coding, debugging, and testing. Created by Anthropic, Claude 3.5 Sonnet has impressed with its streamlined coding process, outstanding reasoning potential, and outstanding context memory.

How to Build a Multi-Agent Orchestrator Using Apache Flink and Apache Kafka

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

The Evolution of Automation: Why Enterprises Are Turning to AI Agents

Process automation has been around for decades, but the tools under this technology umbrella have multiplied over the years. Robotic process automation (RPA) was an early tool for handling simple, routine tasks, and it’s still powerful to have in your intelligent automation arsenal. But when technologies like intelligent document processing, business rules, and workflow orchestration entered the scene, they brought new capabilities to the process automation suite.