AI's output is untrusted input. Validate it, fence it in, and never hand it raw to anything that can do damage — the same reflex you already have for form data.
Learn how to choose the right API testing framework, including REST Assured, Supertest, pytest, Postman, Karate, and Keploy, for better API test automation.
Learn to build a strong testing pipeline to ensure code quality, as in enterprises today most code is generated by AI agents at a huge volume and scale.
React 19's compiler eliminated most of my useMemo and useCallback code. Table virtualization, optimistic updates, and route splitting still need manual attention.
Agentic AI doesn’t replace traditional automation. It uses existing workflows, APIs, and systems as tools while handling decisions, exceptions, and complex tasks.
The latest MCP spec mandates W3C tracing. Use Quarkus and OpenTelemetry to easily visualize disjointed, multi-round-trip AI agent workflows in production.
AI coding assistants are deeply fluent in current Spring conventions, which raises a real question: does that lock architecture into 2020-era patterns? It turns out, no.
The latest MCP updates introduce security risks like protocol confusion. Quarkus mitigates these vectors using strict request filtering and enterprise security layers.
Root searches are cached and easy to access. Filtering forces requests to hit the backend database directly, triggering stricter anti-bot checks that block basic proxies.
AI deployment demos always end before the hard part begins. Writing code is 20% of delivery. This article breaks down the hidden 80% that demos never show you.
CI optimization using Git diff and JGit to selectively run impacted Karate tests, reducing regression execution while preserving safe fallback coverage.
The future of AI agents is searchable tool discovery, not hardcoded APIs. MCP and semantic search are turning tools into capabilities agents can find and use at runtime.
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