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Why edge AI development is still hard  AI is no longer confined to cloud experiments. Developers are increasingly expected to deliver AI inside apps, devices, and edge systems where responsiveness, privacy, resilience, and local control are essential. But building those experiences for production is still difficult.  Teams often have to solve m...

9 min read · June 3, 2026 · Sebastian Kohlmeier    Shipping an AI agent is the easy part. Keeping it accurate, safe, and accountable in production is where teams get stuck. Agents are non-deterministic. Their behavior shifts as models update, tools change, and traffic patterns evolve and most of that drift happens silently, long after the dem...

With hosted agents, we made it straightforward to build and deploy agents on Foundry. You write your logic, run azd deploy, and your agent is live. But “live” and “production-ready” aren’t the same thing. The gap shows up quickly. Your customer support agent handles requests, but it forgets to ask for an order number before looking up status. It...

Memory has always mattered for personalization and continuity. But as customers move agents from demos into production, another requirement becomes just as important: reliability.   Enterprise teams need agents that not only remember facts, but also apply what they have learned to follow procedures consistently, recover from repeated failure mod...

Learn a practical model lifecycle for Microsoft Foundry: select the right model, evaluate quality, optimize cost, operate safely, and improve as production needs change.

Learn how Microsoft Foundry helps developers build, deploy, and operate production-ready agents with Agent Framework, Toolboxes, hosted agents, Microsoft 365 distribution, observability, and agent optimization.

Learn how Foundry IQ helps developers ground agents with unified enterprise knowledge, serverless retrieval, improved agentic retrieval quality, and production-ready security.

Learn how Microsoft helps developers build trustworthy AI agents with open evaluations, portable runtime controls, production observability, and security workflows that work across frameworks.

Your healthcare app needs "La médica" not "El médico." Your legal documents need precise terminology, not generic translations. When domain-specific language matters, generic LLM translation falls short. Azure Translator's adaptive translation lets you teach the model your terminology with just a handful of examples—no model training required. I...

Ship multilingual features in hours, not months Your translation workflow probably looks like this: one vendor for real-time chat, another for document translation, a custom LLM integration for nuanced content, and a spreadsheet tracking which system handles what. Azure Translator's new API (version 2026-06-06, now GA) consolidates all of this...