Retrieval-Augmented Generation (RAG) is the backbone of most enterprise AI applications today. By grounding large language models (LLMs) like GPT-4…
enterprise AI
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When you first build a multi-agent system, the initial architectural focus is usually on logic: getting the Researcher Agent to talk to the Writer…
The Enterprise AI Dilemma: Prototyping vs Production When starting an AI project, the default reflex for any…
Struggling to choose between Azure AI Agents and LangGraph? Uncover the architectural differences in state management, scalability, and operational costs.
When I first compared azure openai vs openai, I assumed the difference was mostly branding.Same models. Same…
When Prompt Engineering Wasn’t Enough The model was smart.The prompts were detailed.The outputs were… inconsistent.Sometimes it answered…
The Dashboard Was Green. The Problem Wasn’t. The model passed every metric.Accuracy was high. Precision looked solid….
The Incident That Didn’t Look Like a Bug The dashboard was green.Latency was fine.Accuracy was high.No exceptions….
As I stand at the crossroads of present capabilities and future possibilities, I find myself both excited and anxious about where AI agent technology is heading. Today, I’m sharing my strategies for building Azure AI agent architectures that can evolve with the rapidly changing landscape.