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Grape handles exceptions with rescue_from blocks declared on your API class: rescue_from :all catches every exception, while rescue_from ActiveRecord::RecordNotFound targets one class. Inside an endpoint, error!("Not Found", 404) halts with a formatted error, defaulting to status 500. Without a handler, invalid params return 400 and other exceptions bubble up to Rack. This guide covers Grape 3.3. Every response body, status code, and error message was verified on Grape 3.3.5, Rack 3.2.7, and Ruby 3.4.10.

You can now ask your coding agent to set up AppSignal in your application. Give it a prompt, let it follow the installation instructions, and watch a checklist fill in as data starts arriving. Installing AppSignal was already straightforward, but it still meant checking your environment, adding the right integration, creating an app, handling credentials, and confirming that telemetry arrived. These are useful checks, but they don't all need your attention.

In the life sciences industry, where breakthroughs in research and healthcare are fueled by data, data silos can be a big problem. Data silos might be caused by things like legacy systems, departmental divisions, disparate data formats, or lack of interoperability standards. Data silos can manifest at any point in the product lifecycle and make it hard for the right people to access and use the information they need, when they need it.

At Insomnia, we believe your tools should support you (and your agents!), not force you into certain ways of working. Over the last few months, we’ve been hard at work streamlining our interface so that it’s easier than ever for you to get started, stay organized, and work however you want with your APIs. There are two key concepts when a developer works with an API.

*Enterprise AI governance requires a composed stack connecting high-level risk and compliance oversight with real-time runtime enforcement. While oversight tools manage inventories, approvals, and evidence, AI gateways apply policy controls directly to live model, API, MCP, and agent traffic. Implementing both runtime controls and structured governance frameworks ensures comprehensive security, observability, and cost management across your entire AI estate.*

For much of the past decade, the PropTech default was straightforward: buy the commodity software and reserve engineering capacity for what differentiated the product. AI-assisted development is moving that line, and the answer looks less obvious than it did two years ago. For the broader framework, including cost, integrations, vendor lock-in, data ownership, and long-term maintenance, see our guide to build vs buy real estate software.