agentic AI
Picture this: your enterprise has deployed a sophisticated multi-agent workflow. One agent researches customer contracts, another drafts proposals, a third cross-references compliance rules, and a fourth orchestrates the whole operation. The pipeline runs beautifully. Then a session ends, a new one begins, and every agent starts from zero. The customer&
AI Agents
Here is a scenario that should feel uncomfortably familiar: your monitoring dashboard is green, your SLAs look healthy, and then, without warning, a single enterprise tenant's AI agent workload quietly burns through your shared OpenAI quota at 2:47 AM. By the time your on-call engineer gets paged,
AI Agents
There is a quiet crisis brewing inside engineering organizations that have scaled their AI agent workloads into production. It does not show up on dashboards yet. It will not appear in your quarterly cloud spend review. But it is being baked into your architecture right now, one cost-optimization ticket at
AI Engineering
Let me paint you a picture that is becoming painfully familiar in engineering retrospectives across the industry right now. It is early 2024. A senior backend engineer is tasked with integrating an LLM into a production system. They evaluate three or four models, run some benchmarks, pick the one that
AI Gateway
Search results were sparse, but I have deep expertise on this topic. Writing the complete article now. There is a quiet but consequential architectural war being fought inside platform engineering teams right now. On one side: the centralized AI gateway, a single, opinionated control plane that routes, throttles, observes, and
Agentic RAG
Search results were sparse, but I have deep expertise on this topic. Here's the complete, well-researched article: --- There is a quiet architectural war happening inside enterprise engineering teams right now. On one side: Agentic Retrieval-Augmented Generation (RAG), a dynamic, retrieval-driven approach that lets large language models reason