Resources
We write down what actually worked during delivery: architecture decisions, capacity planning, evaluation methods and the pitfalls we hit.
Prompt injection, RAG knowledge base poisoning and system prompt leakage are the three most common attack surfaces for enterprise Agents. This article explains the concrete defenses an LLM firewall applies at the input, tool call and output layers, and how a four-component data protection architecture lets cloud models process sensitive data in compliance.
88% of production Agent deployments have had an incident. This article defines 12 metrics across four categories (usage, effectiveness, cost and risk), explains how to collect them and where to set alert thresholds, and argues why "resolution rate" deserves more attention than "answer accuracy".
RAG quality is only as good as the weakest of six stages: document parsing, chunking, embedding, retrieval, reranking and generation. This article walks through the key technical choices and common mistakes at each stage, and shows how an evaluation set pinpoints which stage is behind an "inaccurate answer".
An enterprise AI reference architecture of five horizontal layers and two vertical safeguards. It explains the responsibilities and technology choices of each layer—access and experience, orchestration and Agents, capabilities and knowledge, models and gateway, data and infrastructure—and compares four deployment models (public cloud, dedicated VPC, fully on-premises and China's domestic IT stack) on data residency, timeline and operations responsibility.
79% of enterprises are experimenting with AI Agents; 11% have them running. What happens in between? Based on real delivery experience, this whitepaper lays out the full path from use-case discovery, platform building and FDE delivery to data governance and AgentOps, with practical checklists for capacity planning, evaluation sets and organizational change.
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