AI agents expand attack surfaces, demanding safety by design, advanced red teaming, and shared benchmarks to build secure, trustworthy intelligent systems.
With growing computing power for AI and its misuse in cyberattacks like autonomous exploits, deepfake scams, and smart malware becomes even more worrisome.
Small language models (SLMs) offer 90% of the value of large models at a fraction of the cost. Devs can maximize AI ROI by training SLMs on domain-specific data.
In this article, we will explore the value of AI agents, introduce popular agentic AI platforms, and walk through a hands-on tutorial for building a simple AI agent.
Junior developers are shipping features faster with Cursor and GitHub Copilot, while senior engineers question if AI-assisted code is maintainable at scale.
AI-first backends let LLMs drive dynamic, personalized API logic in real time replacing static rules. Validation and guardrails keep them reliable and secure.
The era of AI autonomously doing the work is here. Agentic AI systems can plan multi-step workflows, make decisions, use tools, and coordinate with other agents.
Learn how developers can use data agents for natural-language querying, Copilot Studio for AI interactions, and real-time intelligence for streaming analytics.
An introductory tutorial for Java developers on writing AI-infused applications using Quarkus with LangChain4j. You don't need Python to write AI apps.
Spring AI agentic patterns show how to coordinate multiple ChatClient calls to LLMs. We look at how Dapr Workflows can make these interactions durable and resilient.
Learn why using Postman's 'Fix test' may mask bugs — tips to investigate failures, preserve test intent, and protect API reliability. Verify, then act.