{"id":28567,"date":"2024-12-23T12:47:06","date_gmt":"2024-12-23T11:47:06","guid":{"rendered":"https:\/\/wordlift.io\/blog\/en\/?p=28567"},"modified":"2025-01-03T12:22:25","modified_gmt":"2025-01-03T11:22:25","slug":"ai-agent-economy","status":"publish","type":"post","link":"https:\/\/wordlift.io\/blog\/en\/ai-agent-economy\/","title":{"rendered":"The Dawn of the AI Agent Economy: Finding Your Ontological Core"},"content":{"rendered":"\n

Executive Summary<\/strong><\/h3>\n\n\n\n

The artificial intelligence landscape is undergoing a fundamental shift. At NeurIPS 2024, Ilya Sutskever made a profound observation that resonates deeply with the future of AI: “Pre-training as we know it will end<\/em>.” This isn’t just another AI prediction \u2013 we’ve reached “peak data<\/em>,” with only “one internet<\/em>” worth of training material available. This limitation is catalyzing a crucial evolution: the rise of reasoning AI agents that emphasize structured knowledge over raw data volume.<\/p>\n\n\n\n

This transformation is particularly significant for the service economy. While traditional software has focused on enhancing productivity, AI agents<\/a> are poised to take over service delivery itself<\/strong>, expanding the total addressable market from mere productivity enhancement to comprehensive service execution \u2013 a shift that opens up opportunities worth over $10 trillion.<\/p>\n\n\n\n

To further illustrate the vast potential of this transformation, it helps to consider the emerging ‘application category layer’<\/em> for enterprises. Recent analysis highlights distinct areas where practical value from AI agents is being realized. These include categories like AI Assistants, directly mirroring the vision of agents like TrenIA (introduced below) transforming customer interactions, and Content<\/a> and Media Generation, where agents like Agent WordLift are already demonstrating impact. <\/p>\n\n\n\n

Through advances in knowledge graphs and GraphRAG technology, organizations can navigate this transition by focusing on their “ontological core<\/em>” \u2013 the fundamental concepts and relationships that define their domain. This approach has shown a 30% improvement in reasoning capabilities compared to traditional approaches, as demonstrated through real-world applications in different application categories (here is a deep dive on the application category layer<\/a> for enterprises).<\/p>\n\n\n\n

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Dominant Generative AI Use Cases – by Menlo Ventures.<\/figcaption><\/figure>\n\n\n\n
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Explore AI Agent Solutions for Your Business:<\/strong> Ready to transform your customer relationships with AI agents? Schedule a free consultation today<\/a> to see how WordLift can help you implement these cutting-edge technologies.<\/p>\n<\/blockquote>\n\n\n\n

The End of the Pre-training Era<\/strong><\/h3>\n\n\n\n

With just “one internet” worth of training material available, the future of AI lies not in accumulating more data, but in better understanding and structuring the knowledge we already have. This stark reality signals a fundamental shift in how AI must evolve, pushing the industry toward systems that can reason, plan, and execute with existing knowledge rather than waiting for more training data.<\/p>\n\n\n\n

This limitation isn’t just a constraint \u2013 it’s an opportunity to fundamentally rethink how we approach artificial intelligence. The focus must shift from raw data accumulation to knowledge structuring and reasoning capabilities. This evolution marks the beginning of a new era in AI, where the quality of knowledge organization and the sophistication of reasoning mechanisms become the primary drivers of advancement.<\/p>\n\n\n\n

The Power of the Ontological Core<\/strong><\/h3>\n\n\n\n

As Tony Seale<\/a> emphasized during his keynote at Connected Data 2024, organizations need to “find their ontological core<\/em>” – the fundamental concepts and relationships that define their domain. This isn’t just about organizing data; it’s about creating a resilient foundation for AI systems.<\/p>\n\n\n\n

With the era of unlimited training data coming to an end, organizations must focus on what Sutskever calls the “fossil fuel of AI<\/em>” \u2013 their existing data and knowledge structures. At WordLift, we’ve discovered that the key to building effective AI agents lies in this ontological core \u2013 the structured representation of an organization’s domain knowledge, relationships, and business logic.<\/p>\n\n\n\n

Our SEO agent<\/a> demonstrates this approach in action. By leveraging semantic technologies and knowledge graphs, we’ve created a system that doesn’t just process content \u2013 it understands it within the broader context of SEO strategy and business objectives. This has enabled teams worldwide to scale their content marketing efforts while maintaining strategic control and oversight.<\/p>\n\n\n\n

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The Ontological Core – a concept introduced by Tony Seale – presentation from Connected Data 2024<\/em>
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The Semantic Layer – Tony Seale on data decentralization – from his presentation at Connected Data 2024<\/em>
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TrenIA: Exploring the Future of AI Agents in Transportation<\/strong><\/h3>\n\n\n\n

While developing Agent WordLift for SEO and content optimization, we began exploring how our agentic technologies could extend beyond digital visibility to transform customer relationships. This led to an experimental project in WordLift’s lab: TrenIA<\/strong>, a conceptual AI agent for Trenitalia<\/em> that demonstrates how ontological cores and knowledge graphs could revolutionize service delivery for Italy’s primary train operator.
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The concept emerged from a simple question: How could we apply our expertise in knowledge graphs and AI agents to enhance real-world customer experiences? By integrating WordLift’s Knowledge Graph architecture with viaggiatreno APIs, we envisioned how an AI agent could transform static train schedules into dynamic, user-focused experiences.<\/p>\n\n\n\n

This exploration wasn’t conducted in a vacuum. Our sister company RedLink<\/a>‘s previous work with Deutsche Bahn<\/strong> on “Reisebuddy<\/em>” provided valuable insights into how AI can augment transportation services. Their experience, started 8 years ago (!!!), reinforced a crucial lesson: AI in customer service must be human-led<\/strong>, with the ontological core centered on passengers, their transportation needs, and the infrastructure that serves them.<\/p>\n\n\n\n

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Deutsche Bahn ‘Reisebuddy’ \u2013 A pioneering personal assistant created in 2015.<\/figcaption><\/figure>\n\n\n\n

The Discovery Problem: How AI Agents Make Rail Travel Intuitive<\/h4>\n\n\n\n

The AI Agent for Train operators tackles a fundamental UX challenge that has plagued train travel websites across all major European rail operators. Look at any rail booking interface today – whether it’s Trenitalia<\/em>, Deutsche Bahn<\/em>, \u00d6BB<\/em>, or NTV<\/em> – and you’ll encounter the same friction point: users must know exact station names to even begin their journey planning<\/strong>. <\/p>\n\n\n\n

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UX challenges that AI will help us fix on the ITALO and \u00d6BB websites.<\/figcaption><\/figure>\n\n\n\n

This rigid approach falls apart in real-world scenarios. A tourist wanting to explore Sicily, a business traveler heading to the Lungau region, or a family looking for the closest station to their holiday destination – all face the same barrier of having to guess or research station names before they can even start their search. The screenshots from these operators’ websites tell the story: dropdown lists of station names without context, no regional guidance, and no intelligence to help users discover appropriate stations for their destinations.<\/p>\n\n\n\n

Through its knowledge graph architecture, TrenIA demonstrates how AI agents can transform this experience. Instead of requiring users to know that they need “Fiumefreddo Sicilia” or “Lungitz\/Gusen Bahnhof”, the system understands geographic relationships, regional contexts, and travel patterns. Users can express their needs naturally – “How do I get to Noto from Milan?” or “What’s the best way to reach the Lungau region?” – and receive intelligent guidance about relevant stations, connections, and travel options. This isn’t just about better search – it’s about bridging the gap between how people think about travel and how rail networks actually operate<\/strong>.<\/p>\n\n\n\n

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How TrenIA help us travel from Milano to Sicily.<\/figcaption><\/figure>\n\n\n\n

This capability, powered by combining geographic ontologies with natural language understanding, points to a future where AI agents can transform rigid, technical systems into intuitive, human-centered services. As we’ve seen with our Agent WordLift in the SEO domain, when AI can understand both user intent and system structure, it can create dramatically better user experiences.<\/p>\n\n\n\n

In designing TrenIA’s framework, we identified the following components of its ontological core<\/em>:<\/p>\n\n\n\n