#RAG
How an AI Agent Works: The Anatomy of a System from Token Prediction to MCP
We examine the inner workings of autonomous neural network agents: how a standard text generator gains hands, memory, and access to real-world tools, why context breaks down, and where the primary risks of automation lie.
Why RAG Breaks: An Analysis of 6 Critical Retrieval Errors and Recipes for a Reliable Architecture
Nearly three-quarters of AI agent failures are caused not by weaknesses in language models, but by errors during data retrieval. We examine RAG's hidden vulnerabilities and how to fix them.
Halving routine tasks and launching services in 7 days: practical outcomes of AI adoption in development
Real-world experience of integrating neural networks into construction fintech: how to automate 65% of incoming invoices, speed up code review to one minute, and set up self-healing automated tests.
Опыт M&T Bank: как масштабная модернизация IT открыла дорогу генеративному ИИ для 15 000 сотрудников
Американский региональный банк M&T Bank развернул корпоративные ИИ-инструменты для большей части своего персонала. Эффект уже заметен на практике: обработка обращений в колл-центре ускорилась на 6 минут на каждый звонок.
Autonomous AI Without Internet Access: Architecture of the Closed Multi-Agent Platform "Archangel"
How to build an enterprise multi-agent AI system in a fully isolated environment based on Kubernetes, an event-driven data bus, and local RAG.