<![CDATA[The Ontology Imperative - Building Trustworthy Agentic AI: The Foundation]]>https://theontologyimperative.substack.com/s/the-foundationhttps://substackcdn.com/image/fetch/$s_!Kirs!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fea6ab8d2-9416-4cf8-b993-a884fcedd086_1262x1262.pngThe Ontology Imperative - Building Trustworthy Agentic AI: The Foundationhttps://theontologyimperative.substack.com/s/the-foundationSubstackSun, 12 Jul 2026 14:03:57 GMT<![CDATA[1b – Own the Ontology or Rent Your Future – The four capability gaps that make agentic AI ungovernable]]>https://theontologyimperative.substack.com/p/own-the-ontology-or-rent-your-futurehttps://theontologyimperative.substack.com/p/own-the-ontology-or-rent-your-futureTue, 27 Jan 2026 11:27:33 GMTReading time: ~12 minutes

Summary

Most knowledge graph initiatives fail before they begin. Not because the technology is immature, but because organizations lack four capabilities: real semantic expertise, leadership that treats ontologies as strategic IP, commitment to open standards, and formal semantics for trust and agentic AI governance. Teams racing to deploy agents without these foundations learn the same lesson: you cannot retrofit accountability.

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Executive Brief

If your board approves agentic AI pilots without addressing four capability gaps, you will discover that governance cannot be retrofitted. By then, the failure will be attributed to leadership, not technology.

The gaps are predictable. You cannot staff semantic expertise. Leadership treats knowledge as plumbing instead of intellectual property. You choose formats that surrender ownership. You confuse higher accuracy with trustworthy autonomy.

Accuracy moves you from wrong to plausible; formal semantics move you from plausible to provable.

Why this matters: Without semantic control, agents operate on rented logic, not institutional authority.

This standard will decide which agents you can trust with authority and which must remain copilots.

Governance debt compounds faster than technical debt when agents act under your authority without accountability infrastructure. If you think this is a tooling decision, you are already behind.

Four Gaps That Sink Knowledge Graphs and Make Agents Ungovernable

The market signals are converging. Capgemini’s 2026 C-suite research highlights the governance gap, Microsoft’s Fabric IQ and Data Agents introduced an explicit ontology layer in the data platform narrative, and the World Economic Forum’s 2025 framework defines progressive, monitored governance for agents.

Part 1a showed why curated enterprise knowledge is the advantage; Part 1b explains why most organizations fail to realize it.

You decide knowledge graphs are strategic. You build the case. Then the initiative stalls. It stalls because you could not staff semantic expertise, leadership treated knowledge as plumbing, you picked formats that lock meaning to a vendor, and you confused accuracy for trust.

The predictable obstacles that prevent organizations from converting data into strategic intelligence

Gap 1 — The Talent Crisis

Your org chart has a gap that no current role fills. Data engineers model structure. Data scientists model patterns. Neither models meaning. Ontology design requires a discipline most organizations have never hired for: formal knowledge representation that makes business logic machine-readable and logically consistent.

This gap is invisible because nobody reports on it. I have watched it stall initiatives from the inside. I helped build early oil and gas ontologies through POSC Caesar, joined the W3C Workshop on Semantic Web in Oil and Gas in 2008, and led the Integrated Operations in the High North (IOHN) program with 23 companies. In every program, the bottleneck was the same: not technology, not budget, but the ability to define meaning with formal precision.

Large language models make ontology work more accessible. They can automate syntax. But only humans can define meaning, authority, and boundaries. LLMs will generate an ontology that looks right and passes syntactic validation. It will fail in production because contradictory inferences were never surfaced. The demand signal is unmistakable; the supply is insufficient.

Where The Capability Actually Lives

Some of the best ontologists have philosophy degrees. That sounds surprising until you realize that representing knowledge requires training in logic and formal reasoning, exactly what analytic philosophy provides. Geoscientists often excel for similar reasons. In oil and gas, we integrate seismic, well, and geological data for billion-dollar decisions. Coming from geophysics, my work meant bridging the fuzziness of geology with the rigor of physics.

The differentiator is not more scripting. It is formal logic and conceptual analysis that surface contradictions and make policy machine-readable. When AI agents hallucinate, the cost is liability and destroyed trust. The people who can prevent that are trained in precisely the disciplines most hiring managers overlook.

Gap 2 — The Hiring Mistakes

A European aerospace manufacturer spent eighteen months and three million euros to learn that structure is not semantics. They hired data engineers to build a supply chain ontology. It modeled the data perfectly. It failed to federate because the meaning of shared concepts was never formally defined.

A financial services firm auto-generated a compliance ontology that passed every syntactic test. It failed in production because contradictory inferences, invisible without formal reasoning, could not be reconciled.

These are not edge cases. They are the predictable outcome of hiring for the wrong capability. Successful organizations hire from three pools: semantic web professionals (RDF/OWL), philosophy graduates with computational training, and domain experts used to formal modeling under uncertainty. The cost of hiring the wrong profile is not a slow start. It is a failed initiative you must restart from scratch.

Gap 3 — The Leadership Gap

If you do not control the semantics, you do not control the intelligence. This is CDO territory. Whoever governs the ontology governs what AI can understand about your business. Control semantics, control enterprise intelligence.

When Data Valuation Reveals The Trap

National Highways in the UK valued their data at sixty billion pounds. What most readers missed was the risk: the knowledge that makes that data valuable sits in a GIS vendor’s proprietary formats.

This represents a valuation trap. When your institutional logic is rented from a platform, that sixty billion pound asset is effectively illiquid. You did not buy a foundation; you rented one. Extraction will be slow, expensive, and in some cases impossible. For a board, this is a fundamental loss of asset control.

The Four Questions That Decide Strategy

Before spending money, successful organizations answered four questions:

  • What domain knowledge are we encoding?

  • Which concepts matter most to our advantage?

  • What are we excluding and why?

  • How will this impact vendor independence?

From Principles to Architecture

The difference is operational governance. Declarative governance says AI should respect privacy; operational governance ensures the agent checks the ontology for rules at runtime and records justifications.

Moving from policy to runtime enforcement is the requirement for scaling autonomous systems

These four gaps do not fail independently. They compound. Without semantic expertise, you cannot build the ontology. Without leadership treating that ontology as strategic IP, the initiative gets deprioritized. Without open standards, the ontology you do build belongs to a vendor. Without formal semantics, the ontology cannot enforce the constraints agents need.

The result is governance debt that accumulates faster than technical debt. Technical debt slows you down. Governance debt exposes you to liability with every autonomous decision an agent makes under your authority. When Part 2a examines what happens when organizations deploy agents without these foundations, the compounding pattern will be visible across industries. The organizations that close these gaps first will deploy agents that their competitors cannot match, not because their models are better, but because their foundations are trustworthy.

Gap 4 — The Standards Decision

Format is an ownership decision. The pipe is not the brain. Without a formal semantic layer, you can route messages but you cannot govern decisions. Connectivity without semantics is just faster error.

Ora Lassila, co-editor of the original RDF specification, used a provocation at Connected Data London in 2025 to make this point: he framed legacy data as anything that is not RDF. Treat what is not RDF as legacy and bring it into formalism rather than bending meaning to storage.

The Architectural Choice That Determines Ownership

Organizations that maintain ownership follow a pattern: they build their authoritative ontology in RDF and OWL as the system of record. Reasoning, validation, and provenance live there. Use property graphs only as derived execution caches.

Meaning lives in RDF/OWL; performance lives in the caches.

Separating the system of meaning from the surface of execution to maintain long-term asset control.

The Hyperscaler Signal

In November 2025, Microsoft introduced Fabric IQ and Data Agents that bring ontological modeling into its data platform narrative. Amazon Neptune supports SPARQL for RDF and Gremlin for property graphs. Treat these as execution surfaces and keep your master ontology in open standards for portability and auditability.

Why SQL Schemas Are Not Enough

In 2024, Air Canada was held liable for misinformation from its chatbot. The tribunal was clear: You are responsible for what your AI says.

  • Text to SQL: 16 percent baseline. Wrong.

  • Knowledge graph retrieval: 54 percent. Plausible.

  • Ontology-governed reasoning: ~80 percent—72 percent correct plus 8 percent explicit “I don’t know” responses that prevent confident hallucination. Provable.

The leap from probabilistic pattern matching to deterministic reasoning required for enterprise trust,

Trustworthy systems must know when to say “I do not know”. This is the work ahead at Viking Life-Saving Equipment, where mission-critical safety operations rely on equipment that is a vessel’s license to operate.

The Audit: Five Questions for the Board

Boards do not need semantic expertise. They need semantic governance. These five questions separate dashboard theater from accountability infrastructure. If your AI steering committee cannot answer them, you have a governance gap that no vendor demo will close.

  1. Can your AI systems trace a decision to its authoritative source? Not to the model that generated it, but to the business rule, policy, or fact that should have governed it. If not, you cannot audit what your agents do. You are delegating authority without accountability.

  2. Who owns the definitions your AI systems use? If the answer is a vendor platform, your institutional knowledge is rented, not owned. That is a strategic dependency masquerading as a technology choice.

  3. Can you distinguish reasoning from hallucination in your AI outputs? If every output looks equally authoritative regardless of whether it reflects institutional knowledge or statistical invention, you have no basis for trust.

  4. Are your AI governance rules encoded in a language machines can execute, or do they exist only in policy documents? Agents can read documents but cannot reason over them consistently. Policy without enforcement architecture is aspiration, not governance.

  5. If an AI decision fails, can you fix the root cause without retraining the model? If not, every failure requires an expensive, time-consuming intervention that scales with the number of agents you deploy.

A fiduciary lens for evaluating AI infrastructure maturity and governance risk.

These are not technical questions. They are fiduciary questions. The board does not need to understand RDF or OWL. It needs to know whether the organization has accountability infrastructure or governance theater.

What To Do Now

Start with a Minimum Viable Ontology (MVO) for the decision that creates the most liability if an agent gets it wrong. Not the easiest decision to model. The one where failure means regulatory exposure, financial loss, or reputational damage. That is where the business case for semantic infrastructure is self-evident.

The pragmatic roadmap for building semantic authority without boiling the ocean.

  • Talent: Your org chart has a gap. Fill it with people who can define meaning with formal precision: semantic web professionals, philosophy graduates with computational training, or domain experts used to formal modeling under uncertainty. Hire for conceptual discipline before technical skills. The wrong hire costs you a failed initiative, not a slow start.

  • Leadership: Treat ontology design as strategy, not plumbing. The CDO who governs the ontology governs what AI can understand about the business. If that responsibility sits with a technology team without executive sponsorship, the initiative will be deprioritized when budgets tighten.

  • Standards: Use RDF/OWL as the meaning layer. This is an ownership decision, not a technology preference. Open standards give you portability, auditability, and vendor independence. Proprietary formats give you speed today and dependency tomorrow.

  • Trust: Require formal semantics. Accuracy without provenance is liability without alibi. The benchmark evidence is clear: ontology-governed reasoning delivers roughly 80 percent accuracy including explicit “I don’t know” responses. That leap from plausible to provable is the standard that determines which agents you can trust with authority.

Next in the series: Part 2a examines what happens when organizations deploy agents without these foundations and how to arrest the spiral. It publishes in two weeks. Subscribe to follow the series.

About The Author

Frédéric Verhelst helps leadership teams build the foundations for non-linear growth. He focuses on ontology-first design and governance for agentic AI. With a PhD in Applied Physics and twenty-five years at the intersection of data, AI, and industrial operations, he has led large semantic interoperability programs, driven digital twin adoption, and advised on multi-billion-dollar decisions. He works at Viking Life-Saving Equipment, where agentic AI governance for mission-critical safety operations is taking shape.

Follow him on LinkedIn for the latest posts in The Ontology Imperative - Building Trustworthy Agentic AI.

Notes and References

Thanks for reading The Ontology Imperative - Building Trustworthy Agentic AI! Subscribe for free to receive new posts and support my work.

]]><![CDATA[1a – The Knowledge Graph Competitive Landscape – What Google, Microsoft, and the Smartest Enterprises Already Know]]>https://theontologyimperative.substack.com/p/1a-knowledge-graph-competitive-landscape-ontology-imperativehttps://theontologyimperative.substack.com/p/1a-knowledge-graph-competitive-landscape-ontology-imperativeSat, 03 Jan 2026 16:34:43 GMTReading time: ~12–14 minutes

Summary

Google's edge is not bigger models. It is fifteen years of curated, machine-readable knowledge that grounds AI in facts. Leaders who invest in that foundation win twice: higher accuracy today and governable autonomy tomorrow.

Subscribe now

Everyone thinks Google’s AI advantage is Gemini. It is not.

It is a compounding asset that predates GenAI by a decade and a half: a curated Knowledge Graph of entities, relationships, and verified facts that systems can query at runtime. Competitors train on similar internet text and hope fluency equals understanding. Google trains and then grounds answers in structured knowledge. That is why seemingly simple questions get answered with confidence, and why complex tasks improve faster when the model can reason over meaning instead of mimicking words.

This changes how you should think about your own AI strategy. Models are replaceable. Institutional knowledge is not.

When your definitions, rules, and relationships live as machine-readable semantics instead of scattered documents and tribal memory, the economics change:

  • Accuracy improves because the meaning is explicit across divisions and systems.

  • Provenance becomes inspectable because facts carry sources.

  • Compliance moves from manual review to software because applicable rules are encoded, not re-interpreted case by case.

  • AI decisions stop hallucinating because there is a factual backbone the system can consult.

That is the business value of knowledge graphs in plain terms.

I see this differently because I have been on both sides of the equation. I built multi-agent systems on ontologies and led a 23-company semantic interoperability program before GenAI existed. Later, as Head of Data Office, I drove digital twin adoption where the underlying principle was identical: systems that act autonomously need machine-readable definitions of what things mean, what rules apply, and where authority ends. The technology evolved. The architectural requirement did not.

The pattern I see in Google’s advantage is the same one that separated successful autonomous operations from failed ones in energy: semantic foundations determine whether autonomy scales or collapses. That lesson now applies to every enterprise deploying AI agents.

From Fluency To Grounding

Large language models created an illusion of understanding. They are brilliant at fluency and pattern completion. They predict the next plausible words based on vast amounts of text. They also invent facts that sound authoritative.

Researchers labeled this phenomenon clearly. The term “stochastic parrot” captured systems that mimic without understanding (Bender and Gebru, March 2021). Vector search can retrieve similar text, but similarity is not meaning. Two documents can be numerically close and logically contradictory.

Knowledge graphs do something different. They encode verified entities and relationships that machines can query with precision. Combine fluency with grounding and you get trustworthy outputs.

Google figured this out fifteen years ago.

Google’s Head Start

This did not appear overnight. In 2010 Google acquired Metaweb and its Freebase knowledge base, then launched the Knowledge Graph publicly in 2012 with 500 million entities and 3.5 billion facts.

By 2016, one third of Google’s 100 billion monthly searches were being answered directly by structured knowledge. That is 33 billion answers per month delivered with confidence because they were grounded in verified facts rather than pattern matching.

By 2020 Google reported 500 billion facts on 5 billion entities. Independent estimates suggest far larger coverage today, although Google does not publish official figures.

The important point is not the exact number. It is that structure compounds. Every new entity creates relationship opportunities with existing entities. Every refined relationship improves precision across the entire graph. A competitor starting today cannot simply scrape faster and catch up.

Board implication: Model capability is replaceable. Semantic foundations compound. Competitive advantage now lives in governance infrastructure, not model scale.

Software ages like fish. Models get retrained, systems get replaced, today’s state-of-the-art becomes tomorrow’s legacy.

But curated, contextualized knowledge with proper semantic foundations ages like wine. It gets more valuable over time as connections deepen and coverage expands.

The Integration That Matters

Google did not replace the Knowledge Graph with large language models. It integrated them.

Public filings show AI Overviews using a custom Gemini model that works in tandem with existing search systems and the Knowledge Graph (DOJ v. Google filings, 2024). The model provides the natural language interface. The graph supplies verified entities, relationships, and facts. Nearly three decades of search quality signals help select authoritative sources.

Fluency on top. Grounding underneath. Gemini provides language. The Knowledge Graph provides truth.

That integration is an advantage you do not recreate quickly with more data, more parameters, or more compute. Only Gemini gets privileged access to this ecosystem.

The Tables Have Turned

Remember when Google seemed caught flat footed by ChatGPT. When the company that invented the Transformer architecture watched OpenAI dominate headlines while Gemini stumbled through public failures.

That was 2023. This is 2026.

On December 2, 2025, Sam Altman sent an internal memo declaring “Code Red.” According to The Information, he warned of “rough vibes” and “temporary economic headwinds” as Google’s Gemini 3 surged in both capability and market adoption. OpenAI postponed advertising initiatives, AI agents for health and shopping, and its personal assistant Pulse to focus all resources on improving ChatGPT.

The irony is striking. Three years ago, Google sounded “Code Red” over ChatGPT. Now Altman is sounding the same alarm about Google.

The most telling metric is factual accuracy. On tests measuring correct, verifiable answers to straightforward questions, Gemini 3 scored nearly double what GPT-5.2 achieved. That gap is not better training techniques or more parameters. It is the Knowledge Graph at work.

Why Curation Beats Volume

Everyone has access to broadly similar raw internet text. OpenAI, Anthropic, Meta, Mistral: they all scrape the web.

The winners are the teams that curate, disambiguate, and connect meaning. The Knowledge Graph improves the signal-to-noise ratio. It identifies authoritative sources by topic. It separates Apple the company from apple the fruit. It grounds answers in facts that can be updated independently of model pretraining.

Google executives credited Gemini’s progress to major improvements in pretraining and data selection in late 2025. The point is simple. Better selection requires better semantics. Curation beats volume when you care about accuracy and accountability.

From FOMO To JOMO

When ChatGPT launched in late 2022, enterprises rushed deployments. Fear of missing out drove pilots into production.

Within months, the reality set in. Accuracy issues, bias, security gaps, and accountability failures forced a reset. The narrative shifted to joy of missing out. Leaders discovered what practitioners already knew. GenAI without grounding hallucinates. You cannot industrialize systems that fabricate facts.

When your AI cites a regulation that does not exist, or recommends a harmful interaction, the cost is liability and destroyed trust. That is why knowledge graph teams suddenly found themselves on speed dial across enterprises.

At IKEA, the LLM team reached out to the Knowledge Graph team for structured knowledge to ground their applications. At Zalando, data scientists who had initially dismissed knowledge graphs requested access. The pattern repeated across enterprises. Initial enthusiasm for pure LLM approaches. Sobering encounters with hallucination. Urgent requests for structured knowledge.

Proof In Production

Leaders who built knowledge graphs before GenAI made a specific architectural choice: encode domain expertise as machine-readable semantics rather than leaving it in documents and heads. That choice now determines which organizations can govern AI agents and which cannot.

Governance Outcomes

Amazon encoded product semantics: not just catalog attributes, but the relationships and rules that govern what complements what and why. The result was a 60 percent improvement in recommendation accuracy. More importantly, when they deploy AI agents for merchandising, the knowledge graph provides the factual backbone those agents consult. Competitors without that foundation must rely on prompts and hope the model gets it right.

IKEA made a parallel choice for interior design expertise. Their knowledge graph encodes which accessories complement which styles, which shapes and materials suit family safety, and how context changes a good recommendation. The business outcome was measurable uplift in attachment rates. The strategic outcome is that this expertise now lives in infrastructure rather than in people who might leave, retire, or be unavailable at scale. When IKEA’s LLM team needed grounding, they reached out to the Knowledge Graph team, because that foundation was already built.

Strategic Outcomes

AstraZeneca encoded relationships between diseases, biological targets, compounds, and clinical outcomes. Drug discovery is fundamentally a knowledge problem. When your competitive advantage depends on connecting signals across vast literature, the organization that can reason over encoded meaning identifies opportunities that pattern matching alone will miss.

Siemens encoded the semantics of industrial assets: what a component is, how components relate, the operating parameters that matter, and the patterns that indicate failure. Their digital twins reason over meaning, not just visuals. Maintenance becomes predictive because the system consults a model of how things work, not a statistical approximation.

Competitive Moat

These are production systems. They share an architectural pattern: domain expertise encoded as machine-readable semantics, extended over years, now serving as the foundation for AI governance. Every one of them started before GenAI. None of them could be replicated quickly by a competitor starting today.

Why Foundations Are Urgent Now

I stepped away from semantic work for nearly a decade. When agents act under authority, hallucination becomes liability, not inconvenience. I returned when GenAI turned ontology from useful to urgent. The technology did not change. The stakes did. When systems merely assisted, foundations were a quality improvement. When they act autonomously under your authority, foundations become a governance requirement. That shift is what makes the next section, the hyperscaler convergence, significant for every board approving AI pilots.

Microsoft’s Governance Bet

Google is not alone. The hyperscalers are converging on the same conclusion.

In November 2025, Microsoft announced at Ignite that it is integrating ontologies into its data fabric to create an intelligence platform. This is a strategic signal. A company that invested billions into large language models is telling the market that models need a semantic backbone.

AWS positions Neptune as graph infrastructure. Google Cloud offers Enterprise Knowledge Graph capability. When the largest cloud providers converge on structured knowledge as foundational technology, it stops being optional infrastructure. It becomes table stakes for trustworthy AI.

The Market Validates This

Analysts reflect the same trajectory. Gartner placed knowledge graphs on the Slope of Enlightenment in its 2024 AI Hype Cycle, past peak hype and through disillusionment into consistent value delivery.

MarketsandMarkets estimated growth from roughly one billion dollars in 2024 to nearly seven billion by 2030. Professional services and consulting are the fastest growing segments because organizations have already concluded they need capability, and they need help building it.

McKinsey’s research on AI high performers shows a consistent pattern. Leaders embed knowledge graphs alongside GenAI rather than treating them as alternatives. Complementary capabilities deliver what neither can alone. That is what mature adoption looks like.

Together, these signals point to the same conclusion: semantic governance is now table stakes for trustworthy AI at scale.

The Leadership Call

You do not need Google’s scale to get Google’s advantage. You need your domain’s semantics encoded so systems can reason over them. The choice is not whether to invest in knowledge graphs. It is whether you control the definitions, rules, and relationships that govern your AI’s understanding of your business, or whether a vendor controls them for you.

Treat your ontology as strategic intellectual property. Use open standards so your institutional memory is portable and auditable.

Build a capability that compounds rather than a project that ends.

Board Motion

Require leadership to produce an inventory of authoritative knowledge domains and identify which of them must become machine‑readable within 12 months.

The cost of waiting is not a missed benchmark. It is governance debt that accumulates every day agents act under your authority without accountability infrastructure. When you deploy AI agents without semantic foundations, every decision they make is one you cannot trace, cannot verify, and cannot fix at the root. That risk compounds faster than technical debt because agents operate at machine speed.

Before you decide how to scale, check what you are actually scaling.

False Comfort Warning

Fluency hides ignorance. Grounding hides nothing.

If your AI strategy relies on prompt fluency without semantic grounding, you are scaling the wrong layer.

What To Do Now

Identify the decision that would create the most value if an AI agent could execute it reliably at scale. That decision is your starting point. Name the entities, relationships, and rules that govern it. This is your first ontology scope.

Build a pilot for that decision that stands on its own as a foundation you will extend. Connect it to GenAI for fluency and keep the knowledge graph in the loop for grounding and traceability.

Measure three things:

  • accuracy against authoritative sources,

  • provenance completeness (can you trace every answer to its origin?), and

  • cycle time improvement.

Engagement metrics tell you people are using the system. These metrics tell you whether they should trust it.

Design the pilot so the semantic foundation compounds. Every entity you encode creates relationship opportunities. Every rule you formalize becomes a constraint an agent can consult at runtime. This is the compounding advantage Google has built for fifteen years. Yours starts with one decision, defined precisely enough that a machine can reason over it.

Key Takeaways

Google’s advantage is curated, machine readable knowledge that grounds answers in facts. Enterprises that pair knowledge graphs with GenAI get trustworthy outputs while fluency only approaches fail. The strategic move is to treat your ontology as IP, use open standards, and build a foundation that compounds.

Next In This Series

Part 1b covers the capability gaps that defeat most initiatives: the talent gap and where to find ontologists, the hiring mistake that sets projects back years, the leadership gap that appears when ontologies are treated as plumbing, and the standards decision that determines who owns your institutional knowledge.

About The Author

Frédéric Verhelst helps leadership teams build the foundations for non-linear growth. He focuses on ontology-first design and governance for agentic AI. With a PhD in Applied Physics and twenty five years at the intersection of data, AI, and industrial operations, he has led large semantic interoperability programs, driven digital twin adoption, and advised on multi billion dollar decisions. He is preparing agentic AI governance for mission-critical safety at Viking Life-Saving Equipment.

Follow him on LinkedIn for the latest posts in The Ontology Imperative.

Sources:

Thanks for reading The Ontology Imperative - Building Trustworthy Agentic AI! Subscribe for free to receive new posts and support my work.

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