Knowledge Hub

An archive of essays on the systems that ground enterprise AI.

Long-form analysis on retrieval architecture, knowledge graphs, agent memory, observability, and the governance of systems that have to be right. Filed when the thinking is done.

001

June 14, 2026

Knowledge Graphs & Ontology

19 min

The Six Graphs Behind an Agent-First Enterprise

Every enterprise deploying agents discovers that a model is only as reliable as the structured context it can reach, and that context lives in graphs. This piece reframes the question from whether you have a knowledge graph to which graphs you build, which you adopt, and which are not yet mature. It names the six graphs an agent-first enterprise needs and sorts them across three ownership tiers: shareable infrastructure, a shared standard with private content, and the irreducibly proprietary graphs that are the moat. It defines the regulatory graph as the most shareable and least built layer, the one that decides whether agents can be trusted in regulated work, and separates the shared law from the proprietary applicability. It closes with a sector read, a decision framework, and a leadership action list, including where deploying broad autonomy today means deploying past the edge of a stack that does not yet exist.

Knowledge Graphs
Enterprise Knowledge Graph
Agentic AI
AI Governance
Ontology Engineering
Decision Provenance

002

June 8, 2026

Governance Risk & Trust

14 min

The Compliance Gate: Why AI Agents Have Already Broken the Review Cycle — and What Replaces It

Enterprise AI adoption is outpacing governance at every level. Most major enterprises have already rolled out AI coding agent licences, and those that haven't have shadow AI filling the gap. Simultaneously, regulators across jurisdictions are enforcing the EU AI Act, NIS2, and ISO 42001 with a speed and precision the compliance industry has not seen before. The result is a widening gap between how fast AI is being built and deployed, and how well organisations can demonstrate they are in control of it. Your compliance model is already broken. The question is whether your board knows it yet.

AI Governance
Agentic AI
LLMOps
AI Observability
Traceability
Auditability

003

June 7, 2026

Core Agent Architecture

18 min

AI Is Already Writing Your Code. Who’s Governing It?

Your developers are already using AI to write code. The governance infrastructure your organization built for human-authored software was not designed for what is happening now — and a policy document is not a substitute for technical controls. This piece introduces the AI Development Governance Framework: four levels that give every board, CIO, and CTO a precise answer to where their organization stands today, what it costs to stay there, and what it takes to move.

Agentic AI
AI Governance
Context Engineering
Context Compression
Agent Orchestration
Multi-Agent Systems

004

June 3, 2026

Core Agent Architecture

12 min

Your Team Is Ready to Go Live. Are You Asking the Right Questions?

Every AI agent go-live conversation asks the wrong question. 'Does it work?' is a question about the past. The decision in front of a business leader is about what the agent does when the world it was built for no longer exists — and no benchmark answers that.

Agentic AI
AI Governance
Knowledge Graphs
RAG
Enterprise Knowledge Graph
Explainable AI (XAI)

005

January 30, 2026

Knowledge Graphs & Ontology

18 min

The Case for a Central Enterprise Ontology

Most enterprise AI is succeeding at the task level — one agent, one job, useful output — and failing at the seam between tasks. When agents from different systems share context or hand off work, the absence of a shared foundation turns visible and expensive: “revenue” means one thing in Power BI and another in Salesforce, one agent's “customer” is a billing account and the next reads it as a CRM contact, and the wrong downstream action follows. The fix is a central enterprise ontology — a formal, queryable model of what the business is, that every tool and agent connects to instead of redefining. Its cost is paid once; its value is collected on every agent deployed after, reversing the usual trajectory where each new system deepens the inconsistency. The discipline is knowing what not to build: the regulatory and industry layers are shared infrastructure, not your IP, so only the enterprise-specific layer is yours — and the place to start is the codebase, where the business is already precisely modelled and the agents creating the risk already run.

Knowledge Graphs
Ontology Engineering
Enterprise Knowledge Graph
Agentic AI
Multi-Agent Systems
Agent Orchestration

006

April 10, 2026

Knowledge Graphs & Ontology

32 min

Knowledge Graph vs Semantic Layer vs SQL/PGQ: The 2026 Enterprise Architecture Guide for AI Agents

Three different things are sold under one name. A full materialised knowledge graph, a semantic layer with a virtual ABox over your existing systems, and a thin SQL/PGQ wrapper that makes a relational database behave like a graph are not interchangeable — they differ wildly in cost, lock-in, and time-to-value, and vendors blur the line on purpose. Agents reset the stakes: a thin wrapper alone gives an agent structure without meaning, and an agent acting on the wrong definition of “Customer” is a remediation incident, not a meeting-room debate. So the semantic layer becomes the minimum grounding layer for any agent of consequence, a full graph earns its cost only where relationships are the product or agent memory needs structure, and the real deliverable is a deliberate hybrid. The discipline that decides success is unglamorous: don't buy the ontology before you hire the ontologist, and don't ship the agent before you buy the ontology.

Knowledge Graphs
Enterprise Knowledge Graph
Semantic Layer
Ontology Engineering
RDF / OWL / SPARQL
GraphRAG

007

May 1, 2026

Retrieval Architecture

14 min

Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval

Most enterprise RAG underperforms on the documents that matter most — contracts, filings, manuals, compliance reports — for one reason: shredding a document into flat chunks throws away where each chunk lived, and the heading hierarchy of a 10-K (Company > Financial Statements > Cash Flows) is itself an implicit knowledge graph the author already built. Proxy-Pointer stops destroying it. It parses headings into a skeleton tree, prepends each chunk's full structural breadcrumb before embedding, then retrieves in two stages — broad semantic recall, followed by an LLM re-ranker that scores candidates on structural relevance rather than surface similarity — and feeds the synthesiser complete sections instead of truncated passages. It needs no GPU and no graph database; it slots into an existing vector stack, and every answer cites the section path it came from, which is an audit property in regulated work. Two honest caveats temper the headline result: the perfect score is on a small, author-built benchmark, and the whole approach is upper-bounded by heading quality — it shines on well-structured documents and falls back to ordinary RAG on scanned or flat ones.

RAG
Structure-Aware Retrieval
Two-Stage Retrieval
Re-Ranking
Vector Databases
Source Grounding

008

May 19, 2026

Enterprise Knowledge Systems

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LLM-Powered Knowledge Graphs for Enterprise Intelligence and Analytics

An enterprise's most valuable knowledge — the decisions made in meetings, the expertise shown in email threads, the commitments buried in chat — is also its least searchable, and it walks out the door when people leave. Vector search alone cannot recover it: ask “who is the expert on Project X?” and you get documents that mention Project X, not a ranked list of people. This research builds an activity-centric knowledge graph from exactly those internal sources, using an LLM to extract entities and infer the cross-silo relationships that were always there but hidden, then uses retrieval as the natural-language way in. The combination answers relational questions neither method can answer alone. Two cautions decide whether it works in practice: LLM relationship inference gets noisy on messy real-world communications, and a unified graph of everything the company says is among the most sensitive artefacts it can build — access governance is a prerequisite, not a later detail.

Knowledge Graphs
Triple Extraction
Entity Resolution
Persistent Memory
Semantic Layer
Auditability