Systems | Development | Analytics | API | Testing

Reusing test cases with Call to Test | Zephyr

SmartBear Zephyr is the Jira-native test management and automation platform that empowers your team to deliver better software,faster. By creating test cases, linking them to user stories and requirements, and monitoring progress all within Jira, you can unify your testing and development efforts. This short video demonstrates how to use a test case in Zephyr, known as the “Call to Test” capability. You’ll see how you can reference and reuse test cases across multiple Jira projects, no matter the test case type.

FastAPI error handling: types, methods, and best practices

Errors and exceptions are inevitable in any software, and FastAPI applications are no exception. Errors can disrupt the normal flow of execution, expose sensitive information, and lead to a poor user experience. Hence, it is important to implement robust error-handling mechanisms in FastAPI applications. In this article, we will discuss the different types of FastAPI errors to help you understand their causes and effects.

The Hidden Cost of Building Your Own LLM Data Layer

For most businesses, the break-even point for self-hosting only makes sense if processing 100–200 million tokens daily. Otherwise, managed API solutions are more cost-effective, faster to deploy, and easier to maintain. Alternatives like DreamFactory offer pre-built, secure API layers, saving time and money while simplifying enterprise AI integration. Bottom line: Building your own LLM data layer is a major investment with hidden challenges.

Security Testing Explained: Protecting Modern Applications And Apis

Security testing helps identify weaknesses in software before attackers can exploit them. It protects sensitive data, ensures system stability, and controls user access. With web, mobile, and API-based applications growing rapidly, security threats are increasing. Security testing helps teams detect risks early, prevent breaches, and meet compliance standards.

From APIs to Agentic Integration: Introducing Kong Context Mesh

The promise of agentic AI is clear: autonomous systems that can reason, plan, and act on your behalf. But there's a fundamental problem standing between that vision and enterprise reality: agents need context to make decisions, and that context lives scattered across your organization. Context is any data — or any abstraction that enables access to data — that an agent needs to do its job. Customer records in your CRM. Inventory levels behind your fulfillment APIs.

Tracking testing progress with reports | Zephyr

SmartBear Zephyr is the Jira-native test management and automation platform that empowers your team to deliver better software, faster. Its reporting and dashboard capabilities provide real-time visibility into your quality metrics, so you always know the status of your release. This short demo video shows you how to navigate the Zephyr Reports tab and build customized Jira Dashboards. Whether it’s via high-level execution summaries or deep-dive traceability reports, you can track coverage and identify testing bottlenecks instantly.

Introducing Agent-Flavored Markdown (AFM): No Code, Portable AI Agents

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.