Net New Markets In The Context of AI Agents & Agentic ‘X’
…and how AI Agents & Agentic ‘X’ Are Shaping the Future of Innovation & Automation…
Some Background
A net new market is a market that emerges due to the creation of an entirely new product category or technology. In contrast to being an extension or modification of an existing market.
It represents previously untapped customer needs or opportunities and typically involves innovation that unlocks demand where none existed before.
In simpler terms, a net new market arises when a business introduces something so novel that it doesn’t compete directly with existing products or services but instead attracts a fresh customer base or creates a new category.
For Example
The Uber phenomenon revolutionised transportation by seamlessly connecting riders and drivers through a simple app-based interface.
This innovation not only disrupted traditional taxi services but also introduced the concept of on-demand convenience powered by technology.
The idea of Uber for X emerged, where businesses apply Uber’s model to deliver services across various industries, from food delivery to home services and logistics…and more.
By leveraging platforms, real-time data and mobile connectivity, these services provide efficient, scalable and customer-centric solutions.
This is different from a business entering an existing market (where competition already exists) or expanding a current market with incremental improvements.
Introduction
The introduction of groundbreaking technologies often sparks a transformative period in software architectures and frameworks.
This is particularly evident with the emergence of AI Agents and Agentic Applications, which are reshaping the landscape of software development.
The journey from innovation to the establishment of a common architecture follows a path characterised by experimentation, standardisation and convergence.
The Initial Phase: Exploration and Innovation
When a new technology such as AI Agents enters the market, developers and organisations experiment with diverse approaches to integrate it into existing systems.
In this phase, there are no dominant patterns or frameworks; instead, a wide range of architectures emerge. These architectures are often tailored to specific use cases, such as automating workflows, enhancing user experiences, or enabling autonomous decision-making.
The notion of a 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺 𝗼𝗳 𝗮𝗴𝗲𝗻𝗰𝘆 with 𝘃𝗮𝗿𝘆𝗶𝗻𝗴 𝗱𝗲𝗴𝗿𝗲𝗲𝘀 𝗼𝗳 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and imbedded in applications gives rise to something I like to refer to as 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗫. Where applications and interfaces are imbued with different levels of agency.
AI Agents, for instance, began with rudimentary frameworks that linked Large Language Models (LLMs) to specific tasks like text generation or data retrieval.
Over time, the integration of reasoning capabilities, tools and external environments like APIs and web interfaces added complexity and opened up new possibilities.
However, the lack of standardisation in how these capabilities were implemented created fragmentation in the market.
The Fragmentation Challenge
In the absence of a common architecture, organisations face challenges in interoperability, scalability and cost-efficiency.
Each implementation may require unique integrations, making it difficult to share components or extend functionality across applications. For example, one AI Agent framework may emphasise reasoning processes, while another focuses on environmental interaction, leading to inconsistencies in how tasks are executed.
This fragmentation is natural in the early stages of any technological innovation. It fosters creativity and competition, enabling developers to explore novel ideas. However, it also underscores the need for convergence to ensure that the technology can achieve widespread adoption.
There is also the fragmentation of service or technology providers in the early stages, as we currently see. Where start-ups are focussing on single elements or technologies for AI Agents, posing a challenges for enterprises from a vendor management perspective.
Moving Toward Standardisation
As the technology matures, certain architectural patterns begin to stand out as more effective or efficient. In the case of AI Agents, the concept of a modular architecture has gained traction.
This involves separating core components — such as reasoning, memory and action spaces — from application-specific tools and integrations. Such modularity allows developers to reuse components and adapt agents to diverse environments without rebuilding the entire system.
Frameworks like ReAct (Reason and Act), which emphasise iterative decision-making loops and tool-based integrations, where agents leverage APIs for specific functions, are examples of emerging standards. These frameworks simplify the development process and promote interoperability across different applications.
The Role of Ecosystem Players
Technology vendors, open-source communities and others play a critical role in driving convergence.
They introduce platforms and frameworks that encapsulate best practices, making it easier for developers to adopt common patterns. Open-source projects, in particular, encourage collaboration and iteration, accelerating the refinement of architectures.
For instance, the integration of tools for reasoning, such as memory management systems, or dynamic interaction mechanisms like web navigation frameworks, is now more streamlined thanks to collaborative efforts.
These tools act as building blocks for Agentic applications, fostering a shared understanding of how AI Agents should operate.
Market Consolidation and the Future
Over time, the market tends to settle on a few dominant architectures that offer a balance of flexibility, performance and ease of use.
These architectures become the foundation for further innovation, allowing developers to focus on application-specific challenges rather than reinventing the wheel.
As AI Agents evolve, the focus will likely shift toward physical embodiments for real-world applications, requiring architectures that integrate sensory data and physical action.
The current common architectures established in the digital realm will serve as a blueprint for these advancements, ensuring a smoother transition.
In Conclusion
The emergence of new technologies like AI Agents sparks a dynamic process of exploration and standardisation.
Through collaboration and competition, the market converges on common architectures, enabling widespread adoption and unlocking the full potential of innovation.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
