The Ontology Revolution

𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗦𝗵𝗶𝗳𝘁 𝗳𝗿𝗼𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀-𝗙𝗶𝗿𝘀𝘁 𝘁𝗼 𝗣𝘂𝗿𝗽𝗼𝘀𝗲-𝗙𝗶𝗿𝘀𝘁 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲

Even the title of this piece has words that you might not have come across yet. What do they mean?

Well, imagine a business is like many teams playing a game.

• 𝗢𝗹𝗱 𝘄𝗮𝘆: Everyone follows a strict list of steps (like a recipe), in their own isolated space. If something unexpected happens, the team gets confused and needs help.
• 𝗡𝗲𝘄 𝘄𝗮𝘆: The team knows the goal (like winning the game), and each player is smart enough to figure out the best move, even if things change.

𝗛𝗼𝘄 𝗱𝗼 𝘁𝗵𝗲𝘆 𝗴𝗲𝘁 𝘀𝗼 𝘀𝗺𝗮𝗿𝘁? 𝗛𝗼𝘄 𝗱𝗼 𝘁𝗵𝗲𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗿𝘂𝗹𝗲𝘀 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝗿 𝗽𝗶𝗰𝘁𝘂𝗿𝗲?

• They use something called an “ontology,” which is like a detailed rulebook, a digital twin, that explains what everything means, how things are connected, and what the rules are.
• With this rulebook, the team (or synthetic “agents”, and I’ll come on to those later) can understand the game and the rules, make good decisions, and work together—even when things get tricky.

𝗦𝗼 𝘄𝗵𝗮𝘁?

• The team doesn’t just follow orders, or the recipe if you will. They understand the goal and can plan, adapt, and solve problems, just like real informed people.
• This makes businesses faster, smarter, and better at reaching their goals, even when things change.

𝗜𝗻 𝘀𝗵𝗼𝗿𝘁: Instead of just following steps, smart computer helpers use a special rulebook (ontology) to understand what’s going on and help the business win the game, no matter what happens!

I’d like to take a step back. There’s always an evolution when it comes to tech. Learn, evolve, enhance. AI is no different. And how it applies to businesses is also no different.

In the rush to roll out AI and autonomous agents into businesses, we’ve often started in either an unstructured way, or a structured way that’s brute force in nature. It’s largely been point solutions, or gimmicky use cases and one foundational layer is often overlooked: 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲. The knowledge that describes the soul of the business.

In complex, domains like insurance, healthcare, and finance, the move from rigid or deterministic workflows to a goal-oriented approach demands more than generative AI, it requires 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗹𝗲 𝗱𝗮𝘁𝗮.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗟𝗟𝗠𝘀: 𝗪𝗵𝘆 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 𝗠𝗮𝘁𝘁𝗲𝗿

LLMs are brilliant at generating plausible responses but blind to structure. They lack an 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗱𝗼𝗺𝗮𝗶𝗻 𝗹𝗼𝗴𝗶𝗰, relationships, and process nuance. These elements are encoded in ontologies and these ontologies can be specified using languages like OWL (Web Ontology Language). Ontologies offer a 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗹𝗮𝘆𝗲𝗿. This is a a living schema of concepts, roles, and relations, which provides grounding, continuity, and precision. They describe an enterprise in a formal living way that can be understood both by human (granted, an educated human) and computer alike.

This is crucial in domains like insurance, where reasoning about entities such as claimants, policies, damages, liability, and timeframes is 𝗻𝗼𝗻-𝘁𝗿𝗶𝘃𝗶𝗮𝗹 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗲𝘅𝘁-𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 and something that needs to be consistent across the entire enterprise. Ontologies encode this knowledge explicitly and systematically, making it computable, queryable, and reusable across applications.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: 𝗙𝗿𝗼𝗺 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 𝘁𝗼 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲

The current wave of AI agents has been dominated by 𝘁𝗼𝗼𝗹-𝗯𝗮𝘀𝗲𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀. In these systems, large language models (LLMs) are tasked with selecting and executing tools, such as APIs, plugins, or scripts, in response to user prompts. Frameworks like LangChain and AutoGen and protocols such as A2A and even to some extent MCP, exemplify this model, where reasoning is approximated by chaining tool calls through templated prompts.

This approach has enabled impressive demos and early use cases. However, beneath the surface, these agents remain 𝗳𝗿𝗮𝗴𝗶𝗹𝗲 𝗮𝗻𝗱 𝗿𝗲𝗮𝗰𝘁𝗶𝘃𝗲. Their behaviour is often governed by narrow rules, implicit assumptions, and prompt-engineered flows. They can sequence tasks, but they cannot truly reason about business context. They lack structured memory, shared understanding, and long-term coherence. In enterprise environments where stakes are high and logic is complex, like insurance claims, regulatory compliance, or supply chain optimisation, this fragility becomes a serious limitation.

To build systems that go beyond surface-level automation, we must introduce a 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻, an understanding of the business as a whole. This is where 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝗶𝗲𝘀 come in. By embedding a formal, machine-interpretable model of the business domain, agents gain access to structured meaning of the entities and relationships in the business. Instead of guessing what a "claimant," "incident," or "coverage window" might imply based on the stochastic model of the LLM, they can operate on those entities directly, the explicit context fed into the model’s understanding, with awareness of their roles, constraints, and relationships.

This capability transforms what agents can do. With an ontology layer, agents are able to:

• 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗰𝗼𝗻𝘁𝗲𝘅𝘁, not just as isolated tool calls
• 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗼𝘁𝗵𝗲𝗿 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 using a shared understanding of business logic
• 𝗠𝗮𝗸𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, not just precedent or prompt syntax
• 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 using the language common to the enterprise

This shift also redefines how we think about 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) and 𝗰𝗮𝗰𝗵𝗲-𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗖𝗔𝗚). In current frameworks, RAG and CAG is often used to inject domain knowledge into prompts by searching a document store or vector database. This adds real-time state and context, such as the user’s previous actions, current system conditions, or business metadata, into the agent’s prompt context.

But when a rich ontology is present, 𝗺𝗮𝗻𝘆 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝗲𝗽𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘀𝘂𝗽𝗲𝗿-𝗽𝗼𝘄𝗲𝗿𝗲𝗱. Instead of retrieving unstructured context, the agent can query the ontology. Instead of re-grounding itself with every prompt, it operates within a stable semantic environment. This leads to three key shifts:

• 𝗥𝗔𝗚 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹𝗹𝘆 𝗮𝘄𝗮𝗿𝗲, grounded in the business context and limited by those guardrails
• 𝗥𝗔𝗚 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗺𝗼𝗿𝗲 𝗽𝗿𝗲𝗰𝗶𝘀𝗲, used selectively for state-aware personalisation
• 𝗧𝗵𝗲 𝗼𝗻𝘁𝗼𝗹𝗼𝗴𝘆 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗹𝗼𝗴𝗶𝗰 𝗮𝗻𝗱 𝗺𝗲𝗺𝗼𝗿𝘆 𝗲𝗻𝗴𝗶𝗻𝗲

In short, we move from agents that call tools to agents that 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗽𝗹𝗮𝗻. Ontologies provide the missing layer of intelligence, turning orchestration into cognition, and transforming agents from task-followers into true collaborators.

𝗧𝗵𝗲 𝗚𝗼𝗮𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀: 𝗔 𝗡𝗲𝘄 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹

Most enterprises today are built around 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀. Business logic is encoded into rigid workflows, checklists, and form-driven interfaces, even in people’s heads. They’re often implemented through ERP systems or industry-specific SaaS products.

These systems are deterministic by design.

They expect the world to conform to a predefined process, and when it doesn't, they break. Exceptions require human escalation, rework, or downstream compensation, they require complex conditional workflows for edge cases and no small amount of operational overheads to maintain.

This model served the early phases of digitization, but it is rapidly approaching its limits.

In contrast, a 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 operates on a different principle. Rather than encoding 𝘩𝘰𝘸 every outcome must be achieved, it focuses on 𝘸𝘩𝘢𝘵 the desired outcome is. It then empowers intelligent agents to figure out the best path dynamically. This model requires systems that can:

• Understand the i𝗻𝘁𝗲𝗻𝘁 behind a goal
• Interpret the 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 in which the goal is being pursued
• Navigate constraints, make trade-offs, and adapt plans over time

This approach mirrors how human experts work. An experienced claims adjuster does not follow a fixed script. They assess context, apply knowledge, infer risk, request more information if needed, and reach decisions aligned with business objectives. A goal-driven system aims to replicate this reasoning capability in software, not by writing more rules, but by equipping agents with structured knowledge and semantic context.

A fundamental difference between an agentic approach to goals and an expert leading this drive is scale.

And, the key enabler of this approach that allows the machine to realistically work towards business objectives is the 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗹𝗮𝘆𝗲𝗿. Ontologies give the system an internal model of the business world. As we’ve established, this means defining what entities exist, how they relate, and what rules govern them. This allows goals to be interpreted in context and enables agents to plan actions that respect both local constraints and global objectives.

In this paradigm:

• Business logic is 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝘁, not hardcoded
• Execution is 𝗮𝗱𝗮𝗽𝘁𝗶𝘃𝗲, not linear
• Coordination is 𝗮𝗴𝗲𝗻𝘁𝗶𝗰, not orchestrated through brittle pipelines

With semantic layers, reasoning models, and ontology-native infrastructure we see that goal-driven businesses are not only possible, they are becoming essential as complexity increases in our enterprises.

As markets shift faster and customer expectations rise, companies that can respond flexibly, reason contextually, and automate intelligently will outpace those bound by deterministic legacy systems.

We are at an inflection point where business are moving from Determinism to becoming Goal-driven.

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝟭.𝟬 - 𝗘𝗥𝗣/𝗦𝗮𝗮𝗦 𝗘𝗿𝗮: 𝗗𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗥𝗶𝗴𝗶𝗱 𝗟𝗼𝗴𝗶𝗰

In the early phase of enterprise automation, organizations were largely dependent on deterministic systems, structured around ERP platforms and SaaS workflows, inherently opinionated software built for the lowest common denominator.

These systems enforced business logic through highly opinionated schemas, predefined processes, and strict compliance rules. While effective for standardizing operations, they left little room for adaptability. Every edge case or process variation had to be explicitly engineered, often requiring expensive professional services or brittle workaround layers. Business logic wasn’t emergent, it was hard-coded. As a result, companies became tightly coupled to the limitations of their software stack, operating more like mechanical machines than adaptive systems.

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝟮.𝟬 - 𝗟𝗟𝗠 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲: 𝗙𝗿𝗼𝗺 𝗦𝘁𝗮𝘁𝗶𝗰 𝗟𝗼𝗴𝗶𝗰 𝘁𝗼 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻

The introduction of large language models (LLMs), generative AI and tool-augmented agents marked a major leap forward. Suddenly, systems could interpret natural language requests and execute complex tasks by invoking tools, calling APIs, generating code, or synthesizing knowledge from documents.

This dynamic execution layer allowed businesses to experiment with more flexible automation, reduce dependency on form-based interfaces, and expand the scope of AI support. However, these systems still lacked true domain intelligence. They responded to prompts, not intentions. They used tools, but without context. They are orchestrated through a workflow rather than reactive to a non-deterministic, educated plan.

Without access to structured knowledge or business models, their behavior remained shallow, useful for one-off tasks, but limited in their capacity to reason or plan over time.

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 𝟯.𝟬 - 𝗢𝗻𝘁𝗼𝗹𝗼𝗴𝘆 + 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗮𝘆𝗲𝗿: 𝗧𝗼𝘄𝗮𝗿𝗱 𝗚𝗼𝗮𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻

As the modern enterprise scales and adapts, the business operating system needs to grow as well. The next stage is defined by 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 and 𝗴𝗼𝗮𝗹-𝗱𝗶𝗿𝗲𝗰𝘁𝗲𝗱 𝗮𝗴𝗲𝗻𝘁𝘀. This is the era of ontology-powered automation, where agents are grounded in domain-specific knowledge encoded in formal ontologies (e.g., OWL).

These ontologies capture not just data structures, but the meanings, relationships, and rules that govern the business. With this layer in place, agents don’t just react. They understand. They can reason about goals, constraints, and timelines. They can decompose complex tasks into subtasks, coordinate with other agents, and adapt when conditions change.

Instead of following rigid workflows, the system dynamically plans and executes actions aligned with business intent. This is where automation stops being a function and becomes an intelligence layer.

𝗕𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮 𝗚𝗼𝗮𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀: 𝗢𝗻𝘁𝗼𝗹𝗼𝗴𝘆 𝗮𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺

The true power of automation does not lie in faster task execution or smarter chat interfaces. It lies in the ability to 𝗮𝗹𝗶𝗴𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘄𝗶𝘁𝗵 𝗴𝗼𝗮𝗹𝘀. That is to create systems that understand what a business is trying to achieve and can act intelligently in pursuit of those outcomes.

This is the promise of becoming a 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀.

In a goal-driven enterprise, automation is not built around predefined process maps or brittle rule chains. Instead, the system is given 𝗶𝗻𝘁𝗲𝗻𝘁, and it autonomously charts a path to the outcome, making decisions, adapting to context, and coordinating across tools and services. It plans, reasons, and learns, just as a skilled human would.

To achieve this, the system must first understand the business. At a deep level. That means not just its workflows, but its 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀. This is where ontologies come in.

Ontologies are not just metadata schemas or taxonomies. They are rich, structured representations of business knowledge. They encode the meanings of things: what a "claim" is, how it relates to a "policy," what conditions constitute "liability," how timelines, actors, exceptions, and thresholds interact. They define the 𝗴𝗿𝗮𝗺𝗺𝗮𝗿 𝗼𝗳 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘄𝗼𝗿𝗹𝗱. This all coalesces into making it intelligible to machines.

When paired with 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗹𝗮𝗿𝗴𝗲 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀, ontologies become a 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗹𝗮𝘆𝗲𝗿 for intelligent systems. They provide grounding, explainability, and direction. They allow agents to:

• Interpret goals in context
• Plan multistep strategies
• React to changing conditions
• Coordinate with other agents toward shared outcomes

The Business Operating System 3.0 operationalises business ontologies, embeds them into agentic planning environments, and equips intelligent agents with the capacity to reason about the world they act in. It is not a chatbot layer. It is a 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝘂𝗯𝘀𝘁𝗿𝗮𝘁𝗲 𝗳𝗼𝗿 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲.

• Business becomes 𝗻𝗼𝗻-𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻, capable of evolving with the market
• Logic is 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗲𝗱 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀, not hardcoded into software
• Automation is 𝗴𝗼𝗮𝗹-𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝗮𝗻𝗱 𝗮𝗴𝗲𝗻𝘁-𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲𝗱, not task-driven and form-bound

This is how the deterministic systems of the ERP era give way to adaptive infrastructures. This is how brittle workflows evolve into intelligent ecosystems. This is how businesses stop automating tasks and start automating outcomes.

And this is how a company become truly 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻.