The AI agent revolution is accelerating faster than many predicted. As illustrated by recent developments from major technology players, we’ve entered an era where building, deploying, and scaling AI agents has become remarkably straightforward. OpenAI recently released a suite of tools specifically designed to simplify the development of agentic applications, including new APIs and SDKs that dramatically lower the barrier to entry for developers looking to create autonomous systems[1]. This democratization poses a fundamental question for businesses: if the technology underpinning AI agents becomes widely accessible, where does sustainable competitive advantage truly reside? The answer may lie not in the agents themselves, but in the systems and data that power them.
The Rapid Democratization of AI Agents
The technological barriers to creating sophisticated AI agents are falling rapidly. OpenAI’s new Responses API combines the simplicity of Chat Completions with powerful tool-use capabilities, allowing developers to solve complex tasks using multiple tools and model turns with a single API call[1]. Similarly, their Agents SDK simplifies the orchestration of multi-agent workflows, making it possible to configure LLMs with clear instructions and built-in tools, manage handoffs between agents, and implement guardrails for input and output validation[1]. This progression means that what once required specialized expertise can increasingly be accomplished with standardized components and frameworks.
This democratization represents a profound shift in how AI capabilities will be distributed across organizations. As Mark Zuckerberg recently predicted, we’re moving toward a world with billions of AI agents—more agents than humans on Earth—where businesses will have core sets of AI agents to choose from, configurable and deployable almost instantly[3]. The implications are clear: when everyone has access to similar agent-building technology, the agents themselves become less of a differentiator. Companies like Box have already demonstrated how quickly these new tools can be leveraged, creating agents that combine web search with internal data access in just a couple of days[1]. This speed of implementation highlights both the opportunity and the challenge—if your competitors can replicate your AI capabilities within days, traditional technological moats disappear.
The Defensibility Challenge in a Democratized AI Landscape
As AI agent technology becomes more accessible, businesses face a fundamental defensibility challenge. When the tools to build sophisticated AI systems are available to all, the ability to create an agent-based solution no longer provides lasting competitive advantage. This pattern has historical precedents in software development, where proprietary systems eventually gave way to commoditized services and open-source frameworks. The difference now is the pace of this transition, which is occurring at an unprecedented scale and speed.
The commoditization risk is particularly acute for single-purpose AI solutions that operate in isolation from broader business systems. Just as we’ve seen with the unbundling of data platforms creating “an operationally fragile data platform that leaves everyone in a constant state of confusion,” isolated AI agents risk creating similar fragmentation[2]. Without integration into core business systems, standalone AI agents become interchangeable parts rather than strategic assets. This reality forces companies to reconsider where their true competitive advantage lies—not in the agents themselves, but in the data and systems that empower them.
Systems of Record: The Foundation for AI Defensibility
As AI capabilities become more democratized, systems of record—the authoritative data sources for business operations like HRIS, ERP, and CRM platforms—emerge as crucial strategic assets. These systems contain the structured, validated data that forms the foundation for effective AI operations. Their value lies not merely in storing information, but in providing the context, relationships, and history that make AI agents truly intelligent within specific organizational contexts.
The importance of these systems is highlighted by the challenges faced in the data platform space, where the proliferation of specialized tools created operational silos and fragmented data landscapes. As described in “Rebundling the Data Platform,” having “this many tools without a coherent, centralized control plane is lunacy, and a terrible endstate for data practitioners and their stakeholders”[2]. The same principle applies to AI agents—without access to a unified, authoritative data foundation, even the most sophisticated agents cannot deliver cohesive, contextual intelligence.
Companies that consolidate and structure their data effectively create a powerful foundation that enhances the capabilities of any AI agent built on top of it. This consolidation provides not just operational efficiency but strategic differentiation—agents working with more comprehensive, better-structured data will consistently outperform those with limited or fragmented data access. As AI becomes commoditized, the quality, completeness, and structure of data becomes the critical differentiator.
The Full-Stack Integration Advantage
Beyond simply owning systems of record, the most defensible position appears to be controlling the entire vertical stack: from the foundational data through the middleware infrastructure to the agent layer and user interfaces. This “rebundling” approach mirrors developments in the data platform space, where tools like Dagster are creating “a unified fabric of assets that serves as the system of record for all the data assets in your platform, and a control plane to operate the computations that produce those assets”[2].
The strategic advantage of this full-stack approach is comprehensive integration. When a company owns both the system of record and the agent ecosystem, it can optimize the entire workflow, ensuring that data flows seamlessly between systems and that agents operate with full context awareness. This integration creates efficiencies that are difficult to replicate with a patchwork of disconnected solutions. As demonstrated by Dagster’s approach to data platforms, having a system that “natively understands the asset-based approach embraced by modern data tools” provides “a massive leap forward for data management”[2].
Klarna’s approach of building tools on top of their internal system of record data store rather than relying on external platforms represents an extreme version of this strategy. While few companies may have the resources to follow this fully in-house approach, it highlights the strategic value of controlling both data and tooling. The question becomes not whether to pursue integration, but how extensively and through what combination of proprietary development and strategic partnerships.
Evolution of Systems of Record in the Age of AI Agents
As AI agents become more central to business operations, the concept of a system of record itself is evolving. Traditional systems focused primarily on storing and providing access to data. In the AI era, systems of record must also capture metadata about agent-driven workflows, automation history, and execution patterns. This expanded scope creates a new dimension of organizational knowledge that goes beyond static data to include dynamic processes and decision histories.
This evolution parallels the shift described in data platforms from “imperative tasks to declarative assets”[2]. Just as modern data platforms are becoming more asset-oriented rather than task-oriented, next-generation systems of record will likely focus more on the relationships between data, processes, and outcomes rather than simply storing information. This creates a powerful form of lock-in, where switching platforms requires not just migrating data but also reestablishing the complex web of agent behaviors and learned patterns that have developed over time.
The concept of “software-defined assets” introduced by Dagster for data platforms provides a useful model for understanding this evolution. By using a system that “enables the explicit modeling and operation of entities” and integrates them into “a unified fabric of assets,” organizations can create a coherent operational environment that becomes increasingly valuable over time[2]. Applied to AI agents, this suggests that the most valuable systems will be those that not only store data but also understand and manage the relationships between data assets and the agents that operate on them.
Marketplace Dynamics: Platform Ecosystem Strategies
A critical element of full-stack integration is the development of agent marketplaces. Companies that control both the system of record and the agent infrastructure can create platforms where third-party developers contribute specialized agents, creating a thriving ecosystem that enhances the value of the core platform. This approach combines the benefits of integration with the innovation advantages of open development.
The marketplace approach aligns with predictions about the future proliferation of AI agents, where businesses might have “a core set of AI agents to choose from, and they can instantly have any number of them configured and deployed instantly”[3]. By controlling the marketplace, platform owners can maintain quality standards while benefiting from the creativity and specialized knowledge of external developers. This creates a powerful network effect, where each new agent increases the value of the platform for all users.
OpenAI’s approach with their Agents SDK illustrates this strategy, providing a framework that “will also work with models from other providers, as long as they provide a Chat Completions style API endpoint”[1]. This balance between openness and control allows for innovation while maintaining the strategic advantages of platform ownership. For businesses building their AI strategy, the question becomes whether to participate in existing marketplaces or attempt to create their own ecosystem.
Strategic Choices: Integrated Platforms vs. Modular Approaches
The tension between fully integrated platforms and modular, API-first approaches represents one of the central strategic questions in the evolving AI landscape. Integrated platforms offer coherence, optimization, and potentially superior user experiences, while modular approaches provide flexibility, specialization, and protection against vendor lock-in.
The experience of data platforms offers relevant insights here. The initial unbundling created operational fragility, with “a constellation of tools, each with their own internal logical model of the assets or entities they manage”[2]. However, complete rebundling might sacrifice the specialized capabilities that made individual tools valuable in the first place. The optimal approach may be what Dagster describes as “a fundamentally new approach to orchestration that orients around assets rather than tasks,” which enables “a fully integrated understanding” while still preserving specialized functionality[2].
For AI agent platforms, this suggests that the most successful approaches may not be either fully integrated or fully modular, but rather architectures that provide strong integration at the data and orchestration layers while allowing for flexibility and specialization at the agent level. This balanced approach would provide the coherence and data advantages of integration while maintaining the innovation benefits of modularity.
Conclusion
As we navigate the rapidly evolving landscape of AI agents, the strategic focus for businesses should be on building or securing access to comprehensive data foundations that can power diverse AI capabilities. The companies that will thrive in this environment will be those that understand that lasting competitive advantage comes not from any individual AI agent but from the integrated systems that combine data, infrastructure, and agent capabilities into coherent, evolving platforms.
For SaaS providers specifically, this suggests a strategic imperative to evolve from point solutions toward platform approaches that own critical data and provide the infrastructure for AI agent deployment. For enterprises, it highlights the importance of data strategy and system integration in preparing for an agent-driven future. And for the technology ecosystem as a whole, it points toward a new round of consolidation as the initial proliferation of specialized AI agents gives way to more integrated, platform-centric approaches.
The billions of AI agents that Mark Zuckerberg envisions will not exist in isolation, but as part of interconnected systems that combine proprietary data, specialized capabilities, and orchestration frameworks. The winners in this new landscape will be those who recognize that in an age of accessible intelligence, the true value lies not in any single agent but in the platforms that bring them together.
Sources
-
New tools for building agents | OpenAI https://openai.com/index/new-tools-for-building-agents/
-
Rebundling the Data Platform | Dagster Blog https://dagster.io/blog/rebundling-the-data-platform
-
Deploying Billions of AI Agents is Easier than You Think - YouTube https://www.youtube.com/watch?v=2Ai7_5G70xY
-
Reimagining Student Success with AI: Unbundling and Rebundling ... https://mainstay.com/blog/reimagining-student-success-with-ai-unbundling-and-rebundling-workflows/
-
How to Build AI Agents for Beginners (2025) - Botpress https://botpress.com/blog/build-ai-agent
-
Records management before and after the AI revolution https://thinkingrecords.co.uk/2020/01/30/records-management-before-and-after-the-ai-revolution/
-
Build and deploy Agents to watsonx.ai from your IDE - IBM https://www.ibm.com/new/announcements/build-and-deploy-agents-to-watsonx-ai-from-your-ide
-
The Great Data Debate: Unbundling or Bundling? https://towardsdatascience.com/the-great-data-debate-unbundling-or-bundling-7d7721ee8514/
-
Build and Recruit Autonomous AI Agents - Relevance AI https://relevanceai.com/agents
-
Ellison's Gospel, The Evolution of Salesforce, and the (AI Driven) Re ... https://newsletter.tidalwaveresearch.com/p/ellisons-gospel-the-evolution-of
-
Unlocking the Potential of AI Agents: From Pilots to Production ... https://www.databricks.com/blog/unlocking-potential-ai-agents-pilots-production-success
-
Meet Tofu The AI Sidekick That's Rebundling and Automating B2B ... https://aimresearch.co/market-industry/meet-tofu-the-ai-sidekick-thats-rebundling-and-automating-b2b-marketing
-
AI Agents: Easier To Build, Harder To Get Right - Forbes https://www.forbes.com/councils/forbestechcouncil/2024/12/12/ai-agents-easier-to-build-harder-to-get-right/
-
My guide on what tools to use to build AI agents (if you are a newb) https://www.reddit.com/r/AI_Agents/comments/1il8b1i/my_guide_on_what_tools_to_use_to_build_ai_agents/
-
Harnessing AI for Strategic Advantage: Unbundling and Rebundling ... https://www.linkedin.com/pulse/harnessing-ai-strategic-advantage-unbundling-rebundling-dr-gary-fox-judce
-
Disrupting systems of record: The generative AI opportunity https://www.squarepeg.vc/blog/disrupting-systems-of-record-the-generative-ai-opportunity
-
AI Is the New System of Record - LinkedIn https://www.linkedin.com/pulse/ai-new-system-record-jose-e-puente-kibff
