Focuses on AI-powered enterprise digitalization, sharing product and technology practices. Covers AI use cases, technical architecture, product design examples, and industry trends. Aimed at developers, product managers, and digital transformation professionals, providing practical solutions and insights. Uses technology to drive digitization and AI to enable business innovation.
This article dissects LangGraph's architecture using a ticket pre-audit trace, explaining how its graph definition, state management, Pregel-based runtime, persistence checkpoints, and observability integrate to enable reliable, recoverable AI agent execution in production systems.
The article analyzes how enterprise AI agents are shifting from prototype development to reliable production workloads, examining four industry approaches, a three-layer platform architecture, and the critical need for semantic assets, reusable capabilities, and continuous operational mechanisms to measure actual work completion rather than usage metrics.
The article analyzes how Salesforce's Claudeforce partnership with Anthropic illustrates a fundamental architectural shift: enterprise software is moving from UI-centric applications to governed capability collections consumable by both humans and AI agents, with Headless 360, MCP, semantic layers, and Skills redefining value delivery.
This article details how to turn ontology‑based semantic assets into a runnable Semantic Service that answers risk queries and suggests actions, using a three‑layer architecture of deterministic code, a versioned knowledge base, and LLM‑driven reasoning, illustrated with a customer health‑score example.
The article analyzes why traditional hand‑over models fail as the number of AI agents grows, proposes a shared‑semantic and result‑driven management approach inspired by Salesforce and Palantir, and outlines a three‑layer organizational model with dual ownership to keep a digital workforce sustainable.
The article explains how to evolve a DDD‑based domain model into a cross‑system ontology that provides AI agents with unified facts, computable logic, and executable actions, using a six‑step process illustrated by a customer‑health‑score case and detailed governance practices.
After a decade of microservices, the article argues that AI agents expose semantic gaps across CRM, ERP, and other systems, and proposes an Ontology Service Layer that unifies objects, relationships, states, metrics, actions, policies, and evidence to enable controlled, auditable AI‑native execution.
The article outlines a deterministic, modular architecture for enterprise AI business‑analysis agents, detailing how to split reports into audited modules, assign clear tool contracts, perform rigorous attribution, generate evidence‑backed insights, and implement robust review, versioning, and evaluation practices.
The article analyzes why directly connecting large language models to a data warehouse often fails to deliver reliable analytics, outlines common failure modes, and presents a structured Skill‑based architecture—including intent routing, parameter compilation, metric‑first querying, review, and governance—to ensure trustworthy enterprise data queries.
The article analyzes how to construct enterprise AI management agents by distinguishing them from personal efficiency agents, defining data semantics and governance, designing two capability chains for query and analysis, and outlining a step‑by‑step implementation roadmap with security, evaluation, and ownership practices.
