When building AI Agents, the compelling case for AI native platforms

In 2024, every organization is faced with the challenge of incorporating AI into their business operations to both improve their key offerings and stay competitive. This shift has prompted many established companies, some founded over the last two decades, to either integrate or acquire AI capabilities. Tracing the lineage of technological advancements—from the PC revolution, through the internet boom, to today's AI transformation—underscores a clear narrative: standalone software applications have historically relied on robust, integrated, and flexible operating systems to succeed.

Synergistic Power of Windows OS and Microsoft Office Integration in the 1990s

When we look back on the technological landscape of the 1990s, it becomes evident that Microsoft Office applications like Excel, Word, and PowerPoint were pivotal in enhancing employee productivity. However, their effectiveness was significantly amplified by their integration with the Windows operating system. This deep integration ensured that these applications could fully leverage the underlying hardware and software capabilities offered by Windows, such as graphical user interface innovations, system-wide clipboard management, and OLE (Object Linking and Embedding) for seamless data sharing between applications. The success of this integration lay in the ethos that Office apps felt like a natural extension of Windows, rather than being retrofitted. This is evident in functionalities such as drag-and-drop, context-sensitive help, and native system notifications. The integration also extends to performance optimization, security, and updates, leveraging Windows' underlying hardware capabilities, security model, and update mechanisms to ensure that both the operating system and Office applications run efficiently and securely. Moreover, the Windows APIs enable extensive customization and extensibility of Office applications, allowing developers to create tailored solutions that enhance productivity and user experience. This deep integration not only improves functionality and stability but also solidifies Office apps as integral components of the Windows ecosystem, providing a cohesive and reliable computing environment for users.

Role of Linux in the Growth of Open-Source Applications in the 2000s

A similar narrative was observed in open source applications and platforms. The evolution of open-source applications like Apache Web Server, MySQL, and PostgreSQL during the 2000s owes much to the Linux platform. This platform's open-source nature has been instrumental in creating an environment conducive to continuous development and collaboration. Under open-source licensing, the global community of developers can inspect, contribute, and enhance the software, which not only increases the robustness, security, and efficiency of these applications but also ensures they meet the specific needs of users. Linux's role is crucial because it supports an ecosystem that prioritizes transparency, community-driven development, and accessibility—key elements for fostering innovation and reliability in software tools. This makes Linux not just a platform option but a fundamental requirement for open-source applications, providing the necessary infrastructure to develop, test, and improve software in a collective, transparent manner.

Strategic Integration of AWS and VMware: Pioneering Cloud Adoption in the 2010s

Moving to the transition brought on by Cloud Services in the 2010s, the pioneer Amazon Web Services (AWS) exemplifies the strategic importance of deep integration between platforms and applications in building a successful cloud ecosystem. In its early days, AWS introduced Amazon S3 (Simple Storage Service), a scalable storage platform designed for the cloud. Recognizing the potential of cloud environments for virtualization, AWS also focused on integrating VMware solutions, thereby enabling users to run VMware-based virtual machines (VMs) directly on AWS infrastructure. This integration was crucial because it allowed existing VMware customers to leverage the cloud's scalability and efficiency without needing to rearchitect their applications or abandon their existing virtualization investments. Enterprises could now transition to the cloud while keeping their familiar tools and workflows, reducing the barrier to cloud adoption significantly. This also paved the way for future innovations and expansions in AWS services, as it demonstrated AWS's commitment to supporting diverse customer needs through deep integration. Moreover, the success of this integration underlined a broader trend: the symbiotic relationship between robust cloud platforms like AWS and versatile applications like VMware can drive technological adoption and open new possibilities for both providers and users. By facilitating a seamless transition to the cloud, AWS not only expanded its own service offerings but also set a standard in the industry for what deep integration between platforms and applications can achieve in terms of scalability, reliability, and user satisfaction.

The Crucial Role of AI Native Platform in Realizing the Potential of AI Agents

In essence it's essential to look at the current AI transformation in this context. AI agents are critical for the AI transformation of enterprises because they serve as pivotal tools in streamlining operations, enhancing decision-making, and improving customer interactions. These agents automate routine tasks (Autonomous AI Agents), reducing the burden on human employees and allowing them to focus on more strategic activities. This automation extends across various domains such as customer service, where AI agents can assist the support agents (Augmented AI Agents), and in data analysis (Analyst AI Agents), where they can process large volumes of data more efficiently than human counterparts. Moreover, AI agents adapt and learn from interactions and data, continually improving their performance and providing insights that can lead to better business outcomes. Their ability to integrate with existing systems and scale according to business needs makes them invaluable in driving operational efficiency, reducing costs, and fostering innovation within enterprises. But these AI agents need an AI native platform to realize all the above mentioned objectives. If we don’t build our AI agents on an AI native platform, the AI agents will be extremely bespoke, fragile and highly unleveraged due to lack of deep integration of AI capabilities. Such agents might still perform basic tasks and mimic conversational abilities, but without the underlying AI-native architecture, they are essentially glorified chatbots. These chatbots are limited by the constraints of non-AI-native systems, where AI functionalities are more superficially tacked on rather than deeply embedded. As a result, they can't leverage the full spectrum of AI's potential, such as contextual understanding, continuous learning, and predictive analytics.

ServiceNow, founded in 2004 and C3.ai, founded in 2009, exemplify two distinct strategic approaches to scaling a business within the tech industry. ServiceNow embraced a platform-based approach, focusing on delivering a versatile and expansive platform that supports a wide array of industries for IT service management and more. This approach enabled ServiceNow to rapidly expand its ecosystem and capabilities, facilitating customization and integration for a diverse client base, thereby driving its growth from $19 million market cap in 2009 to an impressive $159 billion market cap by 2024. Conversely, C3.ai concentrated on crafting bespoke AI solutions tailored to specific enterprise needs, specializing in complex, industry-specific challenges. Although this approach allowed C3.ai to establish a strong presence in its niche, it restricted the company’s scalability compared to ServiceNow, leading to a growth to $3.2 billion valuation by 2024. This divergence highlights how a platform-centric strategy can significantly broaden a company's reach and impact, offering greater scalability and flexibility compared to a focus on customized, project-based solutions.

Therefore, for enterprises aiming to harness the transformative power of AI truly, investing in AI-native platforms is imperative. These platforms do not just enhance the functionality of AI agents; they enable these agents to act as true extensions of the enterprise's intelligence and operational capabilities, rather than mere automated responders. This foundational difference is what distinguishes a genuinely impactful AI agent from a retrofitted AI bot. In the coming posts we deep dive into how DevRev’s AI- native platform AgentOS powers AI agents to realize the true potential of AI for enterprises.

This is part 1 in a series of blogs, case- study, whitepaper and podcasts on “AgentOS”. Stay tuned…