Frontline architect. Sharing knowledge and insights in enterprise architecture.
Huawei's 'Intelligent World 2035' outlines a five-level AI agent maturity model from L1 tool assistance to L5 autonomous wisdom, specifying capabilities, scenarios, upgrade thresholds, and metrics for each level, with 2035 predictions for L4 scale deployment in mature industries.
The article critiques the 'data is the new oil' metaphor, explaining that unlike oil, data lacks standardization, cannot be directly traded, and its value depends entirely on business context. It argues data teams must stop showcasing integration efforts as value and instead demonstrate how data drives concrete business outcomes through collaboration with business units.
The article argues that advancing foundation models are displacing shallow vertical AI tools but not true enterprise-grade agents, which are defined by six requirements including reliability, integration, governance, and security. Enterprise agents survive by owning execution responsibility — calling real APIs, enforcing compliance, and fitting into operational frameworks — while foundation models supply cognition. The future is a collaborative multi-agent system where foundation models handle understanding and vertical agents manage process closure.
This article explains why AI agents will become the primary form of AI-native applications, contrasting tool-based plugins with goal-driven, autonomous agents that maintain memory and enable task delegation over manual tool orchestration.
The article argues that enterprise AI Agent systems fail due to compound error rates in multi-step execution and unsustainable token costs from context-based memory, advocating for architectural rigor—explicit state modeling, stateless execution, and clear boundaries—where AI handles unstructured interpretation while deterministic systems manage reliable execution.
This article breaks down Greg Coquillo's eight-layer architecture for agentic AI, detailing each layer from infrastructure and agent networking to cognition, memory, applications, and governance, explaining how they collectively enable scalable, intelligent, and governable AI agents.
This article analyzes five core differences between B2B and B2C AI agents — user needs, environment, delivery standards, fault tolerance, and business logic — showing why enterprise agents require ROI-driven, stable, and controllable deployment unlike experience-focused consumer agents.
This article presents a four-phase, twelve-activity methodology for designing business objects, covering requirements analysis, object identification through abstraction and merging, validation for rationality completeness and integration, and finalization with ownership assignment to create governable enterprise data assets.
This article presents a structured three-step methodology for business process optimization—organizing current processes into a unified framework, diagnosing root causes using tools like 5 Whys and PFMEA, and redesigning processes via the ESIA method (Eliminate, Simplify, Integrate, Automate) with pilot-based implementation and continuous improvement.
This article traces the concept of Business Objects from its ambiguous international origins through Huawei's enterprise architecture methodology and IBM's 2021 definition, detailing four core characteristics—indispensable business elements, unique identity, independent attributes, and master/transaction data instances—and three key values: defining responsibility boundaries, assigning data ownership, and guiding IT system design.
