This article explores how ontology-driven semantic infrastructure enables controllable Agent execution in Harness Engineering, detailing architecture constraints, context engineering, feedback loops, and the Knora platform's implementation across manufacturing and energy sectors.
This article explores how ontology-driven architecture provides a semantic foundation for controllable AI agent execution, detailing the Knora platform's three-layer design, LPG-based ontology modeling, cognitive engine mediation, and real-world deployments in energy and manufacturing that achieve 70x efficiency gains.
The article presents ontology as the semantic infrastructure for controllable Agent execution, replacing fragile prompt-based constraints with structured business models that enable architecture constraints, context engineering, and verifiable feedback loops, illustrated by the Knora platform's three-layer design and real-world 70x efficiency gains in railway reporting.
The article explains how ontology-driven architecture provides semantic infrastructure for controllable AI Agent execution, detailing three pillars (architectural constraints, context engineering, feedback loops), the Knora platform's layered architecture, and real-world deployments showing 70x efficiency gains.
The article presents an ontology-driven approach to controllable Agent execution, replacing external prompt-based constraints with internal semantic structures. It details three pillars—architectural constraints, context engineering, and feedback loops—implemented in the Knora platform using a labeled property graph ontology layer, cognitive engine, and Agent execution layer, with a manufacturing case study showing 70x efficiency gains.
This article explores how ontology-driven architecture solves the uncontrollability of LLM agents in enterprise settings by replacing external prompt-based constraints with explicit semantic modeling, detailing Knora's three-layer system that enables deterministic verification, precise context retrieval, and continuous ontology evolution from execution feedback, with real-world 70x efficiency gains in railway reporting.
The article analyses why current AI agents often act beyond business rules, proposes an ontology‑driven Harness Engineering framework that provides built‑in architectural constraints, context engineering, and a verifiable feedback loop, and demonstrates its practical realization through the Knora platform with real‑world case studies.
The article examines Harness Engineering's ontology‑driven semantic foundation for AI agents, outlining the challenges of uncontrolled agents, multi‑dimensional safety requirements, architectural constraints, context engineering, feedback loops, and the Knora implementation that bridges technical control to business‑level governance.
