AI agents show superhuman skill yet still fail in simple ways — a paradox known as the “Swiss cheese problem.” The solution lies in neuro-symbolic integration: combining neural networks’ creativity with the rigour of symbolic logic. Knowledge Graphs provide the missing backbone enterprises need for reliable, trustworthy AI.

Knowledge graphs are moving from niche to mainstream. Early adopters who embrace ontologies and semantic layers are already seeing measurable business impact. Here’s guidance for building your own successful knowledge graph.

Vibe coding has its thrills, but LLMs shine when paired with formal specifications. Learn how test-driven practices, ontologies, and knowledge graphs turn ambiguity into executable, structured intent.

Bigger context windows don’t automatically improve retrieval. Real gains come from reasoned, precise context—structured and guided by ontologies and knowledge graphs.

95% of enterprise GenAI pilots fail—but it’s not the models’ fault. The winners connect data, enforce clear semantics, and wrap LLMs in formal ontologies for trustworthy, validated AI.

The Semantic Web isn’t the problem—distributed data integration is. For AI agents to act and reason effectively, organisations need clear semantics, stable URIs, and shared ontologies baked into their data products.

Walmart’s SuperAgents highlight the surface magic of AI orchestration. But the real value lies beneath: shared semantics, aligned ontologies, and a long-term investment in knowledge graphs that let agents coordinate reliably.

With GPT-5, the synergy between LLMs and ontologies is clearer than ever. Larger context, multimodal input, and tool use let models help build ontologies — and ontologies, in turn, strengthen LLM reasoning, creating a self-reinforcing loop of improvement.

‘Semantics’ is often misused, yet it defines the very essence of meaning. Large Language Models hold a latent, globalised semantics — but not yours. True differentiation lies in owning your meaning through Knowledge Graphs and ontologies that reflect your reality, not the world’s average.

‘Semantics’ is often misused, yet it defines the very essence of meaning. Large Language Models hold a latent, globalised semantics — but not yours. True differentiation lies in owning your meaning through Knowledge Graphs and ontologies that reflect your reality, not the world’s average.

Large language models excel at transduction — drawing analogies across cases — and hint at induction, learning patterns from data. But true reasoning demands abduction: generating structured explanations. By pairing LLMs with ontologies and symbolic logic, organisations can move beyond fuzzy resemblance toward grounded, conceptual intelligence.

Even if large language models grasp deep concepts like symmetry or linearity, their internal representations remain messy and entangled — opaque to human understanding. This lack of structure may limit their ability to generalise far beyond training data. Pairing them with well-organised, ontological frameworks offers a path toward clarity, verifiability, and deeper reasoning.

AI agents that work with knowledge graphs aren’t just operating inside a static environment — they’re helping to build it. This co-creative relationship mirrors the dynamics of active inference, where intelligence emerges from continuous interaction between beliefs and reality. The real differentiator isn’t better algorithms, but better ontologies — clearer models of the world aligned with organisational strategy.

Organisations behave like dissipative systems — optimising locally while exporting entropy to the whole. To become AI-native, we must redraw our boundaries, dissolving silos and integrating intelligence at the organisational level. Knowledge Graphs provide the connective tissue for this transformation, turning chaos into coherence.

From SAP and Netflix to ServiceNow and Samsung, leading organisations are embracing knowledge graphs and ontologies as foundational infrastructure for AI and analytics. The reason is clear: AI needs context, businesses need unified meaning, and users need semantic access to insights. The knowledge graph is becoming the new enterprise standard.

All functioning marketplaces rely on shared standards — and data marketplaces are no exception. The key lies in universal identifiers. Borrowing from the Semantic Web, the use of resolvable URLs for data items offers a simple, scalable way to unify fragmented data estates and enable decentralised coordination across the enterprise.

Forward-thinking organisations are realising that their AI strategy is their data strategy. The next frontier isn’t extracting intelligence from data — it’s embedding intelligence back into it. By using AI to shape, connect, and structure data through ontologies and knowledge graphs, we create systems that truly reflect how the business thinks.

Every graph professional eventually meets the Hairball of Hell — the tangled mess that emerges when beautiful graphs outgrow human perception. While AI can thrive in this complexity, we can’t. The solution isn’t more visualisation; it’s meaning. Ontology acts as a semantic scaffold, helping both humans and machines navigate complexity with purpose.

If software is eating the world and AI is eating software, what eats AI? The answer is philosophy—expressed through a clear, machine-readable ontology. By surfacing your enterprise’s core semantics and linking them to real data, you give AI a lens to reason over what truly matters: how your business creates value.

DeepMind’s AlphaEvolve blends LLM creativity with automated evaluation, unlocking breakthroughs from new matrix multiplication algorithms to global compute optimisation. Its success signals a new era for verifiable AI — and shows why enterprises need semantic frameworks like ontologies and knowledge graphs to bring similar rigour to complex business domains.