Snowflake Databricks Agentic Client System of Intelligence Analysis Ontology
A lightweight ontology for modeling the vendor-centric System of Intelligence approach (Snowflake, Databricks) versus the open Semantic Web hyperlink-based approach (Kingsley Uyi Idehen) for enterprise agentic AI infrastructure.
System of Intelligence
A backend layer that models enterprise data, business rules, and tacit organizational knowledge so that both humans and agents can understand and act upon it. Represents the emerging intelligent backend for agentic AI.
A backend intelligence layer that harmonizes analytic data, converts siloed application logic into shared business rules, and captures institutional knowledge for agentic AI.
Agentic Client
The new intelligent client or system of engagement through which business users, builders, and agents interact with data and get work done. Examples include Snowflake CoWork, Databricks Genie, Microsoft Copilot.
An agent-based system of engagement that acts as the intelligent client for enterprise data work, enabling natural language interaction, skill creation, and collaborative knowledge capture.
Semantic Web Hyperlink Approach
Kingsley Uyi Idehen's approach to enterprise data integration using HTTP URIs as stable identifiers and hyperlinks for loosely coupling identity, identification, authentication, authorization, and data spaces (databases, knowledge bases, filesystems, and APIs) across organizational boundaries.
An open, standards-based approach using HTTP URIs, hyperlinks, RDF, WebID, and Linked Data principles for decentralized enterprise data integration without vendor lock-in.
Vendor-Centric Approach
A platform-centric approach where a single vendor (e.g. Snowflake, Databricks, Microsoft) builds a tightly-coupled System of Intelligence and agentic client ecosystem that is proprietary and optimized for that vendor's platform.
A tightly-coupled, platform-vendor approach where the System of Intelligence and agentic client are co-designed and optimized for a specific vendor's infrastructure.
has vendor
Associates an approach or system with its primary vendor or platform provider.
Property linking a System of Intelligence or approach to its vendor organization.
has approach type
Indicates the architectural approach type: tightly-coupled vendor-centric or loosely-coupled open standards.
Property classifying an entity's architectural approach as either vendor-centric or open Semantic Web.
has coupling mechanism
Describes the coupling mechanism used for identity, identification, authentication, authorization, and data space integration.
Property describing how identity, authentication, authorization, and data spaces are coupled -- tightly via vendor platform or loosely via hyperlinks.
has maturity level
Indicates the maturity level of a System of Intelligence implementation on a 9-level scale.
Property indicating the 1-9 maturity level of a System of Intelligence implementation.
kg-generator skill
Knowledge Graph Generator skill for transforming content into comprehensive RDF-Turtle or JSON-LD knowledge graphs using schema.org vocabulary.
rdf-infographic-skill
RDF Infographic skill for generating interactive HTML infographics and Markdown companions from RDF data.
317 | Breaking Analysis | Snowflake, Databricks and the Model Makers: The Battle for the Agentic Client and AI Backend
Snowflake, Databricks and the Model Makers: The Battle for the Agentic Client and AI Backend
Analysis of the converging battle around the agentic client and System of Intelligence backend, framed through Snowflake versus Databricks, Microsoft, Google, OpenAI, Anthropic and others, with comparison to the Semantic Web hyperlink-based approach.
Agentic AI is being misread as separate battles. The larger fight converges on who owns the new intelligent client and the AI backend. This analysis examines Snowflake's CoWork, CoCo, Horizon Context, Cortex Sense and the System of Intelligence framework, contrasted with Kingsley Uyi Idehen's open Semantic Web hyperlink-based approach.
Integrated innovation and extreme co-design
Agentic AI, System of Intelligence, Snowflake, Databricks, semantic web, hyperlinks, knowledge graph, enterprise AI, platform co-design, Federation of Intelligence
theCUBE Research
Formerly known as Wikibon, theCUBE Research provides cutting-edge research, analysis, insights and media for enterprise technology.
SiliconANGLE Media, Inc.
Parent company of theCUBE Research, a voice of enterprise and emerging tech.
David Vellante
David
Vellante
Co-Founder and Chief Analyst
Co-Founder and Chief Analyst at theCUBE Research. Co-CEO of SiliconANGLE Media. Long-time tech industry analyst, entrepreneur, writer and speaker. Co-host of theCUBE.
George Gilbert
George
Gilbert
Lead Data and Analytics Analyst
Lead data and analytics analyst for theCUBE Research. Former Gartner analyst, former lead enterprise software analyst for Credit Suisse First Boston. Co-founded Techalphapartners. BA in economics from Harvard University.
Harvard University
Private Ivy League research university in Cambridge, Massachusetts.
Snowflake Inc.
Cloud data platform company. Provides data warehouse, data lake, and AI/ML capabilities. Creator of Snowflake CoWork, CoCo, Horizon Context, and Cortex Sense.
Databricks Inc.
Data and AI company. Creator of the Lakehouse architecture, Unity Catalog, Delta Lake, and Databricks Genie.
Benoit Dageville
Co-founder and former CEO of Snowflake Inc.
Thierry Cruanes
Co-founder of Snowflake Inc.
Sridhar Ramaswamy
CEO
CEO of Snowflake Inc. Former SVP of AI at Google.
Microsoft
Multinational technology company. Creator of Microsoft Copilot, Fabric IQ, and Work IQ.
Google
Multinational technology company. Creator of Google Gemini Enterprise.
OpenAI
AI research and deployment company. Creator of ChatGPT and Codex.
Anthropic
AI safety company. Creator of Claude and Cowork.
Salesforce Inc.
Cloud-based software company specializing in customer relationship management. Creator of Salesforce Data Cloud and Agentforce.
SAP SE
Multinational enterprise resource planning software company. Creator of SAP Business Data Cloud.
Celonis SE
Global leader in execution management and process mining.
ServiceNow Inc.
Cloud-based workflow automation platform company.
Virtuoso Universal Server
A high-performance and scalable Multi-Model RDBMS, Data Integration Middleware, Linked Data Deployment, and HTTP Application Server Platform created by OpenLink Software.
Database Management, Data Integration, Linked Data
Kingsley Uyi Idehen
Kingsley
Uyi
Idehen
Founder and CEO
Founder and CEO of OpenLink Software. Creator of Virtuoso. Semantic Web pioneer and advocate of hyperlink-based loosely-coupled identity, identification, authentication, authorization, and data space integration.
OpenLink Software
Creator of Virtuoso, a high-performance platform for data management, integration, and Semantic Web applications.
The emerging intelligent backend that models enterprise data, business rules, and tacit organizational knowledge for humans and agents. The most valuable real estate in the AI software stack.
Tightly-coupled to vendor platform (Snowflake, Databricks, Microsoft, etc.)
The new system of engagement through which business users, builders, and agents interact with enterprise data. Includes Snowflake CoWork, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, ChatGPT/Codex, Claude/Cowork.
Tightly-coupled to backend System of Intelligence via co-design
Snowflake CoWork
Snowflake's business-user agentic client for deep research across enterprise data, artifacts and dashboards, collaborative knowledge capture, skills and reusable workflows, and external application hooks.
Snowflake CoCo
Snowflake's builder-oriented client for developers, data engineers, analysts and technical builders. Formerly Cortex Code.
Snowflake Horizon Context
Snowflake's context layer that collects metadata from diverse systems and enriches it with lineage, popularity, semantic views, business glossary, descriptions and tags, then activates that context for CoCo, CoWork, Cortex Agents and BI tools.
Snowflake Cortex Sense
Snowflake's institutional memory layer that enriches Horizon Context by pulling in unstructured knowledge, ambient context, skills, artifacts and user memory. Early move toward capturing tacit knowledge.
Databricks Genie
Databricks' agentic client for natural language interaction with enterprise data.
Semantic Web Hyperlink-Based Approach
Kingsley Uyi Idehen's approach using HTTP URIs as stable identifiers and hyperlinks for loosely coupling identity, identification, authentication, authorization, and data spaces (databases, knowledge bases, filesystems, and APIs). Uses RDF for data space integration, WebID for decentralized identity, and Linked Data principles for data mesh integration without vendor lock-in, due to its use of existing open standards.
Loosely-coupled via HTTP URIs and hyperlinks for identity, identification, authentication, authorization, and data spaces
Federation of Intelligence
Kingsley Uyi Idehen's vision of a decentralized, federated System of Intelligence built on Linked Data principles, where enterprise intelligence emerges from loosely-coupled hyperlinked data spaces rather than a centralized vendor platform.
Loosely-coupled via HTTP URIs, WebID, RDF, and Linked Data across decentralized data spaces
Comparison: Vendor-Centric System of Intelligence vs Semantic Web Hyperlink Approach
A direct comparison between the Snowflake/Databricks vendor-centric tightly-coupled System of Intelligence approach and Kingsley Uyi Idehen's open Semantic Web hyperlink-based loosely-coupled approach to enterprise AI infrastructure.
The vendor-centric approach (Snowflake, Databricks, Microsoft, etc.) builds a tightly-coupled System of Intelligence where the agentic client and intelligent backend are co-designed on a single vendor platform. Identity, governance, authentication, and data integration are managed within the vendor's ecosystem. The Semantic Web hyperlink approach uses HTTP URIs as stable identifiers and hyperlinks for loosely coupling identity, identification, authentication, authorization, and data spaces (databases, knowledge bases, filesystems, and APIs). Key differences: (1) Coupling: tight vs loose; (2) Identity: vendor-managed vs HTTP-URI-based; (3) Integration: platform-mediated vs hyperlink-mediated; (4) Vendor lock-in: inherent vs eliminated; (5) Maturity: rapidly commercializing vs standards-based and decentralized.
Vendor-Centric System of Intelligence
The approach taken by Snowflake, Databricks, Microsoft, and others where the System of Intelligence is built as a tightly-coupled platform with proprietary agentic clients and governed within a single vendor ecosystem.
Tightly-coupled via vendor platform ecosystem
System of Intelligence Framework
The five-layer System of Intelligence model: Mapping, Rules, Institutional Memory, Decision Guidance, and Learning and Feedback.
Agentic Client Landscape
The emerging agentic clients including Snowflake CoWork, CoCo, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, ChatGPT/Codex, and Claude/Cowork.
Nine-Stage Maturity Model
The nine-level progression from siloed aggregate snapshots through enterprise knowledge graph to process-as-data.
Kingsley Uyi Idehen's approach to building a Federation of Intelligence using HTTP URIs, hyperlinks, RDF, WebID, and Linked Data principles for loosely-coupled enterprise data integration.
Frequently Asked Questions about the Agentic Client and System of Intelligence Battle
Twelve frequently asked questions covering the Snowflake/Databricks battle for the agentic client and AI backend, with comparisons to the Semantic Web hyperlink-based approach.
What is the System of Intelligence?
What is the System of Intelligence and why is it the most valuable layer in the AI software stack?
System of Intelligence explained
The System of Intelligence is the emerging intelligent backend that models enterprise data, business rules, and tacit organizational knowledge so that both humans and agents can understand and act upon it. It has five layers: Mapping (catalog and metadata), Rules (semantic views and governance), Institutional Memory (tacit knowledge and context), Decision Guidance (recommendations and advice), and Learning and Feedback (continuous improvement from agent traces and human corrections). Snowflake's Horizon Context and Cortex Sense are early implementations of this vision.
What is the agentic client?
What is the agentic client and how does it relate to the System of Intelligence?
Agentic client explained
The agentic client is the new system of engagement through which business users, builders, and agents interact with enterprise data. Examples include Snowflake CoWork (for business users), Snowflake CoCo (for builders), Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, OpenAI Codex, and Claude Cowork. The agentic client and the System of Intelligence backend must be co-designed because the backend learns from interactions that happen in the client, creating a tight feedback loop for continuous improvement.
How does Snowflake CoWork differ from CoCo?
What is the difference between Snowflake CoWork and Snowflake CoCo?
CoWork vs CoCo differences
Snowflake CoWork (formerly Snowflake Intelligence) is aimed at business users and knowledge workers, providing deep research across enterprise data, artifacts and dashboards, collaborative knowledge capture, skills and reusable workflows, and external application hooks. Snowflake CoCo (formerly Cortex Code) is aimed at developers, data engineers, analysts and technical builders. Together they give Snowflake two feedback channels into the intelligent backend -- one from business users and one from builders.
What is the nine-stage maturity model?
What are the nine stages of System of Intelligence maturity?
Nine-stage maturity model explained
The nine levels are: 1 Siloed aggregate snapshots (classic BI), 2 Canonical entity resolution (one customer, one product), 3 Temporal event context (streaming events), 4 Behavioral abstractions (pattern recognition), 5 Probabilistic predictions as data (churn scores), 6 Enterprise knowledge graph (web of related entities), 7 Modeled actions (preconditions and effects), 8 Live state representation (real-time business model), 9 Process as data (operating logic managed as data). Levels 1-6 focus on context; levels 7-9 cross into operational intelligence.
What is Horizon Context?
What is Snowflake Horizon Context and how does it serve the System of Intelligence?
Horizon Context explained
Snowflake Horizon Context is a context layer that collects metadata from Snowflake, data lakes, SaaS systems, databases, BI and ETL tools, then enriches it with lineage, popularity, semantic views, business glossary, descriptions and tags, and activates that context back through CoCo, CoWork, Cortex Agents and BI tools. It represents Snowflake's move from cataloging to active business context. However, it is still in early stages -- strongest at metadata, lineage, and governance, but needs to evolve toward a full ontology that captures business process rules and relationships.
What is Cortex Sense?
What is Snowflake Cortex Sense and how does it capture institutional memory?
Cortex Sense explained
Snowflake Cortex Sense is an institutional memory layer that enriches Horizon Context by pulling in unstructured knowledge, ambient context, skills, artifacts and user memory. It is Snowflake's early move toward capturing tacit knowledge -- the accumulated context around how work gets done, what people mean, which definitions are authoritative, what users repeatedly correct, and which workflows become reusable. It sits at approximately level 3 of the nine-stage maturity model.
What does co-design mean in this context?
What does the co-design principle mean for the agentic client and System of Intelligence?
Co-design principle explained
The co-design principle, drawn from Clay Christensen's integrated innovation and Jensen Huang's extreme co-design, holds that the intelligent client and System of Intelligence backend must be built together because each teaches the other. The client needs the backend for context and trust; the backend needs the client because user and builder interactions are how the system learns how work actually gets done. This is why Snowflake's CoWork and CoCo are strategically important -- they are not just front ends but teaching surfaces that feed the intelligence layer.
How does the Semantic Web hyperlink approach compare to Snowflake's System of Intelligence?
How does Kingsley Uyi Idehen's Semantic Web hyperlink-based approach compare to Snowflake's vendor-centric System of Intelligence?
Semantic Web vs Snowflake comparison
Kingsley Uyi Idehen's Semantic Web hyperlink approach uses HTTP URIs as stable identifiers and hyperlinks for loosely coupling identity, identification, authentication, authorization, and data spaces (databases, knowledge bases, filesystems, and APIs) -- creating a Federation of Intelligence without vendor lock-in. Snowflake's approach builds a centralized System of Intelligence tightly-coupled to its platform, where identity, governance, and data integration are managed within the Snowflake ecosystem. The key contrast is loose coupling via open Web standards versus tight coupling via a proprietary platform. Snowflake offers rapid integration and governed consistency; the Semantic Web approach offers decentralized autonomy, cross-platform interoperability, and freedom from vendor lock-in.
How do hyperlinks enable loosely-coupled enterprise integration?
What are the advantages of using hyperlinks for loosely coupling identity, authentication, and authorization compared to vendor-tightly-coupled approaches?
Hyperlink coupling advantages
Hyperlinks as stable HTTP URIs enable loosely-coupled integration by: (1) making every entity (person, organization, data space) resolvable via a standard web protocol; (2) decoupling identity from any single vendor's identity management system; (3) enabling authorization decisions based on WebID and linked data relationships rather than platform-specific roles; (4) allowing data spaces to be integrated across organizational boundaries through RDF link traversal rather than ETL pipelines; (5) eliminating vendor lock-in because any HTTP-URI-identified entity can be accessed by any standards-compliant tool. This contrasts with Snowflake's approach where identity, governance, and data access are mediated through and dependent on the Snowflake platform.
How does WebID address the silo problem?
How do WebID and Linked Data principles address the enterprise silo problem differently than Snowflake's Horizon Context?
WebID vs Horizon Context for silos
Snowflake's Horizon Context addresses silos by pulling metadata from diverse systems into a centralized context layer enriched with lineage, popularity, and semantic views -- but it remains dependent on the Snowflake platform as the integration hub. WebID and Linked Data principles address silos differently: WebID provides decentralized identity and authentication where any entity can authenticate across any system without a central identity provider, and Linked Data principles allow data spaces to be integrated through hyperlink traversal across organizational boundaries. The Semantic Web approach eliminates the hub-and-spoke model entirely in favor of a peer-to-peer mesh of hyperlinked data spaces, which is more resilient and naturally cross-organizational but requires more sophisticated infrastructure adoption.
What can agents safely do at each maturity level?
How does agent autonomy scale across the nine maturity levels?
Agent autonomy by maturity level
At L1-2: answer questions and generate dashboards (human decides). At L3-4: make segment-level recommendations (human approves). At L5-6: provide individualized quantified recommendations (human oversees). At L7: discover and compose actions within governance bounds. At L8: act against live business state with substitution logic. At L9: recommend improvements to the business process itself. Agent autonomy increases as the business model's fidelity and scope mature.
Which approach is better: vendor-centric or open Semantic Web?
Which approach -- vendor-centric System of Intelligence or open Semantic Web hyperlink approach -- is better positioned for the agentic enterprise?
Vendor vs Semantic Web positioning
Both approaches have distinct advantages. The vendor-centric approach (Snowflake, Databricks, Microsoft) offers faster time-to-value, integrated governance, consistent user experience, and enterprise-grade support contracts -- ideal for organizations that prioritize operational simplicity and can accept platform dependency. The open Semantic Web hyperlink approach (Kingsley Uyi Idehen) offers maximum flexibility, zero vendor lock-in, cross-organizational interoperability, and decentralized autonomy -- ideal for organizations that need to integrate across heterogeneous environments and value long-term data sovereignty. The winning enterprise strategy may combine both: using vendor platforms for operational efficiency while adopting Semantic Web standards (HTTP URIs, RDF, WebID) for cross-platform identity, integration, and data space federation.
Glossary of Key Terms: Agentic Client and System of Intelligence
Key terms defining the battle for the agentic client and AI backend, with references to Semantic Web concepts and the nine-stage maturity model.
System of Intelligence (SoI)
The emerging intelligent backend layer that models enterprise data, business rules, and tacit organizational knowledge so that both humans and agents can understand and act upon it. Contrasts with the Semantic Web approach which envisions a decentralized Federation of Intelligence.
The backend intelligence layer that harmonizes siloed analytic data, converts application logic into shared business rules, and captures institutional knowledge for agentic AI.
The new system of engagement through which business users, builders, and agents interact with enterprise data. Examples include Snowflake CoWork, Databricks Genie, Microsoft Copilot, Google Gemini Enterprise, OpenAI Codex, and Claude Cowork.
An agent-based intelligent client that serves as the primary interaction surface for enterprise data work, enabling natural language queries, skill creation, artifact generation, and collaborative knowledge capture.
CoWork
Snowflake's business-user agentic client offering deep research across enterprise data, artifacts and dashboards, collaborative knowledge capture, skills and reusable workflows, and external application hooks. Formerly called Snowflake Intelligence.
Snowflake's business-facing agentic client that serves as a teaching surface for the System of Intelligence by capturing questions, corrections, artifacts, and workflows.
CoCo
Snowflake's builder-facing development client that enables technical users to create pipelines, semantic views, and agent workflows that feed the System of Intelligence.
Horizon Context
Snowflake's context layer that collects metadata from diverse systems, enriches it with lineage, popularity, semantic views, business glossary, descriptions and tags, and activates it for CoCo, CoWork, Cortex Agents and BI tools.
Snowflake's metadata enrichment and activation layer that turns catalog metadata into active context for AI, BI, applications and agents.
Cortex Sense
Snowflake's institutional memory layer that enriches Horizon Context by pulling in unstructured knowledge, ambient context, skills, artifacts and user memory. An early move toward capturing tacit organizational knowledge.
Snowflake's tacit knowledge capture layer that preserves unstructured context, skills, artifacts, and user memory as part of the System of Intelligence.
Kingsley Uyi Idehen's approach using HTTP URIs as stable identifiers and hyperlinks for loosely coupling identity, identification, authentication, authorization, and data spaces (databases, knowledge bases, filesystems, and APIs). Uses RDF, WebID, and Linked Data principles for decentralized enterprise integration without vendor lock-in, due to its use of existing open standards.
An open, standards-based approach to enterprise data integration using HTTP URIs, hyperlinks, RDF, WebID, and Linked Data principles for loosely-coupled, decentralized, vendor-independent data space federation.
WebID
A decentralized identity and authentication protocol based on HTTP URIs. Each WebID is an HTTP URI that identifies a person, organization, or agent and serves as the basis for authentication via TLS or other mechanisms. Enables cross-platform identity without a central identity provider.
A decentralized identity protocol using HTTP URIs for cross-platform authentication and identification without vendor lock-in.
Linked Data
A set of best practices for publishing structured data on the Web using HTTP URIs and RDF, enabling data from different sources to be connected and queried. The four principles: use URIs as names, use HTTP URIs for dereferencing, provide useful information upon dereference, and include links to related data.
Web standards-based best practices for publishing and connecting structured data across distributed sources using HTTP URIs and RDF.
Co-Design
The principle that the intelligent client and intelligent backend must be developed together because each teaches the other. Drawn from Clay Christensen's integrated innovation and Jensen Huang's extreme co-design philosophy.
The co-development of the agentic client and System of Intelligence backend as a tightly-coupled feedback loop, where client interactions train the intelligence layer and the intelligence layer provides context for client actions.
Reasoning Trace
A structured record of the steps, premises, and inferences an AI agent or human reasoner follows to reach a conclusion. In the System of Intelligence, reasoning traces are captured as RDF graphs to enable auditability, replay, and continuous improvement of the intelligence layer.
An auditable sequence of logical steps and evidence linking premises to conclusions, enabling verification and learning from agent decisions.
Level 1: Siloed Aggregate Snapshots
Classic BI reporting — departmental cubes, metrics, and dimensions. Semantic views and metric definitions live here. The enterprise operates in a descriptive 'what happened?' analytics mode with human-driven action.
The first maturity stage where data exists in siloed departmental snapshots with no cross-domain context or entity resolution.
Level 2: Canonical Entity Resolution
The enterprise harmonizes entities — one Customer, one Account, one Product across the estate. Master Data Management becomes foundational for AI because agents need a consistent view of entities they reason about.
The maturity stage where canonical entity resolution enables cross-departmental data aggregation and consistent entity identity.
Level 3: Temporal Event Context
Events become first-class data. Streaming updates and real-time event flows update tables and context continuously. The system understands sequences of events, not just snapshots.
The maturity stage where temporal event context and streaming data enable real-time business state awareness.
Level 4: Behavioral Abstractions
The system classifies behavior — high-value shoppers, likely churners, suspected fraudsters. Patterns get named. Diagnostic analytics asking 'why' becomes possible.
The maturity stage where behavioral pattern recognition and abstraction enable diagnostic analytics.
Level 5: Probabilistic Predictions as Data
Predictions become part of the data model. Each entity carries continuously updated forecasts — churn risk, repurchase probability, etc. Prescriptive analytics enables recommendations with quantified confidence.
The maturity stage where probabilistic predictions are modeled as first-class data for prescriptive analytics.
Level 6: Enterprise Knowledge Graph, as a Semantic Web
The model represents a web of related things informed by an Ontology — canonical customer established by deterministic reasoning and inference and associated with other canonical associated entities like orders, SKUs, promotions, inventory, fulfillment, carriers, payment status. Relationships are traversable (i.e., lookup friendly or dereferencable). Collectively, this expresses how metrics relate to business entities, in deterministic fashion. AI Agents and Skills can now answer richer 'why' questions.
The maturity stage where an enterprise knowledge graph, informed by an ontology, links entities via deterministic reasoning for traversable semantic reasoning, including associated traces and lessons learned.
Level 7: Modeled Actions and Preconditions
The business models actions with preconditions and effects. Agents can reason about action spaces within governed boundaries — apply a promotion, reserve inventory, authorize payment.
The maturity stage where business actions are modeled with preconditions and effects for agent reasoning.
Level 8: Live State Representation
The model becomes a live representation of the business. The state of every customer, inventory, payment, and fulfillment path is represented in real time. The SoI becomes the operational substrate.
The maturity stage where live business state representation enables real-time operational awareness.
Level 9: Process Definitions as Data
The rules about how the business runs are managed as data — not buried in code. Changing a business process becomes a data update. Analytics can optimize the process itself.
The maturity stage where business process definitions are managed as data for continuous optimization.
How to Build an Enterprise System of Intelligence Using Semantic Web Hyperlink Principles
A seven-step guide to building an enterprise System of Intelligence that combines the best of the vendor-centric and open Semantic Web approaches, emphasizing loose coupling via hyperlinks while achieving governed enterprise intelligence.
Variable depending on enterprise scale
Define enterprise entities with stable HTTP URIs
Identify all core enterprise entities (customers, products, orders, suppliers, employees, locations) and assign each a stable HTTP URI. Follow Linked Data principle 1: use URIs as names for things. Ensure URIs are dereferenceable and resolvable via standard web protocols. This creates the foundation where entities are identified independently of any single vendor platform, enabling cross-platform entity resolution at maturity level 2 of the nine-stage model.
Establish WebID-based decentralized identity
Deploy WebID for every person, agent, and organization in the enterprise. Each WebID is an HTTP URI that serves as a decentralized identity for authentication and authorization. Unlike vendor-managed identity (e.g. Snowflake RBAC), WebID enables cross-platform authentication without a central identity provider. Use WebID-TLS or WebID-OIDC for secure, decentralized access control across databases, knowledge bases, filesystems, and APIs.
Map data spaces using RDF and Linked Data
Represent all enterprise data spaces -- databases, knowledge bases, filesystems, and APIs -- as RDF graphs. Use schema.org vocabulary for interoperability. Each graph is identified by an HTTP URI and linked to other graphs through RDF triples. This replaces the Snowflake Horizon Context model of centralized metadata enrichment with a decentralized mesh of hyperlinked data spaces. Implement SPARQL endpoints for federated query across graphs, enabling the Federation of Intelligence vision.
Implement hyperlink-based authorization
Design an authorization model where access decisions are based on WebID relationships and linked data graph traversal rather than platform-specific roles and permissions. Use ACLs expressed in RDF (Web Access Control) where authorized agents are identified by their WebID URIs. This enables fine-grained, cross-platform authorization that naturally extends across organizational boundaries. Contrast this with Snowflake's approach where authorization is tightly coupled to the platform's RBAC system.
Create semantic views and business glossaries
Define semantic views and business glossaries using RDF and SKOS, modeled on Snowflake's Horizon Context approach but published as Linked Data rather than locked in a proprietary catalog. Each business term (customer, revenue, churn, active user) gets an HTTP URI with rdfs:label, rdfs:comment, and skos:definition. Semantic views become SPARQL-queryable RDF graphs rather than platform-specific metadata. This enables the same business meaning enrichment as Horizon Context but in a cross-platform, standards-based format.
Build the System of Intelligence feedback loop
Implement the co-design feedback loop between the agentic client and the System of Intelligence using open standards. Agentic clients (CoWork-like interfaces) interact with enterprise data through SPARQL queries and WebID-authenticated access. Every question, correction, artifact, skill, and agent trace generates RDF annotations that enrich the enterprise knowledge graph. Use the Observe acquisition pattern: capture agent reasoning traces as RDF named graphs, evaluate them against rubrics expressed as SHACL shapes, and feed corrections back into the graph. This mirrors Snowflake's Cortex Sense approach but using open, cross-platform standards.
Govern and evolve the enterprise knowledge graph
Establish governance policies for the hyperlinked enterprise knowledge graph using fine-grained, attribute-based access controls (ABAC) that apply equally to human users and AI agents. Define policies based on subject attributes (role, department, clearance, security group, agent capability profile), resource attributes (data classification, graph, entity type, predicate), and environmental conditions (time, location, authentication method, purpose of access). Layer these on top of the WebID-based identity and hyperlink-based authorization foundations — every access decision evaluates the agent's WebID attributes against policy rules expressed as RDF triples. Use SHACL for shape validation and OWL for ontology evolution. Add hyperlink-based cross-references between entity URIs across organizational boundaries. Promote reusable skills to governed ontology components. Continuously evolve the knowledge graph through the nine-stage maturity model, progressing from siloed snapshots through enterprise knowledge graph to process-as-data. The goal is a live Federation of Intelligence that combines governed data gravity with Semantic Web-style loosely-coupled cross-platform interoperability, where access policies are as portable and hyperlinked as the data itself.
More agents than fleas on a camel
Satirical slide showing the proliferation of agents and agent development tools in the market.
The proliferation of agents and agent development tools creates new silos.
AI software stack architecture
Diagram showing the emerging AI software stack with system of agency, system of engagement, system of intelligence, data platform, and systems of record layers.
The AI software stack showing how the system of engagement and system of intelligence layers are converging.
Five-layer System of Intelligence model
Diagram showing the five-layer System of Intelligence model: Mapping, Rules, Institutional Memory, Decision Guidance, and Learning and Feedback, mapped to Snowflake capabilities.
The five-layer System of Intelligence with Snowflake capability mapping.
Clayton M. Christensen
Clayton
Christensen
Harvard Business School professor and author of The Innovator's Dilemma. His theory of disruptive innovation and integrated innovation frames the co-design of agentic client and System of Intelligence.
The Innovator's Dilemma
Clayton Christensen's seminal work on disruptive innovation, referenced in the article as a framing device for the integrated innovation of agentic client and System of Intelligence.
Jensen Huang
Jensen
Huang
CEO of NVIDIA. His concept of extreme co-design is applied to enterprise software in the article: the front end and back end of the AI stack must be designed together.
Tim Berners-Lee
Tim
Berners-Lee
Inventor of the World Wide Web, creator of the Semantic Web vision, and advocate for Linked Data principles. His work underpins the hyperlink-based approach to loosely-coupled identity and data integration.
Semantic Web
An extension of the World Wide Web through standards by the World Wide Web Consortium that promotes common data formats and exchange protocols for web-based data. The Semantic Web vision provides the foundation for Kingsley Idehen's hyperlink-based approach.
Resource Description Framework (RDF)
A W3C standard for representing web-accessible resources and their relationships as subject-predicate-object triples. Used as the core data model for the Semantic Web hyperlink approach.
HTTP URIs / Hyperlinks
Using HTTP URIs as stable identifiers and hyperlinks for loosely-coupled identity, identification, authentication, authorization, and data space integration across databases, knowledge bases, filesystems, and APIs.
WebID Identity
A decentralized identity protocol that uses HTTP URIs to identify people, organizations, and agents, enabling federated, cross-platform authentication without a central identity provider.
A set of best practices for publishing and connecting structured data on the Web using RDF and HTTP URIs, enabling data mesh integration without vendor lock-in.