Agentic AI success requires platforms that can serve as immutable sources of truth while providing real-time streaming capabilities, contextual event organization, and the ability to rehydrate historical data for new model requirements.
The infrastructure decisions enterprises make today will determine which organizations can effectively deploy specialized and agentic AI at scale and which will remain constrained by inflexible, batch-oriented architectures. As artificial intelligence evolves from general-purpose models like ChatGPT to highly specialized, domain-specific systems, most companies are discovering that their current data infrastructure simply cannot support the real-time, contextual requirements of intelligent agents.
But here’s what most organizations miss: this isn’t just about upgrading existing systems. The shift to vertical AI demands a fundamentally different approach to data infrastructure, one that treats every piece of data as an event as an immutable, chronological source of truth that can be contextualized and rehydrated for different model requirements in real-time.
This transformation mirrors the media industry’s evolution in the 1960s from broad publications like Time and Life to specialized magazines targeting specific audiences like Men’s Health and Outdoor. But unlike media specialization, AI’s vertical shift demands fundamental changes to how enterprises manage, contextualize, and deliver data in real-time.
The Reality of Enterprise AI Deployment
Most CIOs are pragmatically focused on optimizing their existing hyperscaler investments: AWS Bedrock and SageMaker, Google Vertex AI, or Azure ML. The goal is straightforward: maximize the value of committed cloud spend while leveraging familiar operational expertise and in-house capabilities. While platforms like Salesforce and SAP offer embedded AI capabilities, adoption remains surprisingly low. This isn’t due to a lack of interest, but rather the immense resources required for these ISVs to keep pace with AI innovation outside their core competencies.
Specialized infrastructure providers like CoreWeave and Lambda Labs are gaining significant traction among organizations with specific performance requirements or specialized model training needs. More significantly, we’re seeing vertical platforms emerge for highly regulated industries: BenevolentAI and Atomwise in pharmaceuticals, specialized solutions in financial services, and sector-specific platforms that sit atop hyperscaler infrastructure. This trend toward verticalization reflects the growing recognition that domain-specific intelligence often delivers more value than general-purpose solutions for targeted use cases.
The investment patterns reveal a telling story. Enterprises that have heavily invested in established platforms are naturally exploring how to leverage their existing infrastructure for AI capabilities. The relatively low adoption rates of embedded AI features suggest these solutions remain too limited to provide substantial business value, particularly given the rapid pace of innovation in the broader AI ecosystem.
Every AI system, regardless of its specialization, depends on originated data that must be contextualized, organized, historically preserved, and replayed. The extent to which these three are done well massively impacts AI outcomes. A customer support LLM focused on GE dishwashers doesn’t need United Airlines flight schedules, but it does require access to relevant appliance data, maintenance histories, and contextual information about related products like refrigerators or home power systems. The capability to maintain an immutable source of truth that can intelligently contextualize events, organize them chronologically, and infer relevance becomes the foundation upon which successful AI infrastructure must be built.
See also: Data Pipelines in the Age of Agentic AI
The Agent Platform Evolution
The development of AI agent platforms represents the next frontier, though it remains in early stages with considerable market noise. AI agent frameworks like LangChain/LangGraph, LlamaIndex, and HuggingFace are capturing significant developer mindshare, while ISVs build agentic point solutions on top of these frameworks. This explosion of tools creates a substantial shadow IT problem for CIOs: dozens of specialized applications that lack standardized governance, deployment, and security approaches.
The proliferation of sales engagement and customer service tools with embedded chatbot agents exemplifies this challenge. Each department may select different solutions, creating a fragmented landscape that becomes increasingly difficult to manage, secure, and optimize. The administrative overhead alone can overwhelm IT departments that lack proper governance frameworks for AI tool adoption.
Looking ahead to the second half of 2025 and beyond, the winners will be platforms that offer flexibility across diverse use cases while leveraging infrastructure and tools already familiar to DevOps teams. Solutions like Sema4.ai demonstrate this approach effectively, combining their original RPA heritage from Robocorp with LangGraph to provide inference and agentic workflow structure. Their model allows enterprises to define agent workflows by creating runbooks written in natural language, bringing business users and analysts closer to implementation design while maintaining technical rigor through familiar AWS-hosted control planes.
The Data Infrastructure Gap
Here’s where most AI strategies fundamentally fail: they underestimate the data infrastructure requirements of intelligent agents. Traditional batch ETL pipelines and point-to-point integrations simply cannot support the real-time, contextual data needs of sophisticated agent workflows. The limitations become apparent quickly when organizations attempt to scale beyond pilot projects.
This creates a specific infrastructure requirement that has only recently become available: streaming data platforms designed specifically for AI agent consumption. Unlike traditional data warehouses or lakes, these systems must serve as comprehensive sources of truth, capturing all data and events not just for current model deployment, but for training, evaluation, and the inevitable model iterations that follow. The technology exists, but adoption remains limited as most enterprises don’t yet recognize this as a fundamental requirement rather than a nice-to-have capability.
Consider the complexity involved: modern AI systems require the ability to process data from any application, whether internal or external to the organization, across various microservices and through standard protocols like Kafka streams. This isn’t merely about data volume; it’s about maintaining data integrity and contextual relevance across diverse sources while ensuring real-time availability.
What makes this particularly challenging is that traditional streaming platforms weren’t designed for AI workloads. They lack the ability to serve as both a streaming data platform and an immutable historical record that can contextualize events and maintain chronological integrity across multiple data sources. The few solutions that can handle these dual requirements represent a new category of infrastructure that most enterprises haven’t yet recognized as essential.
Historical data management becomes essential in this context, but not in the way most organizations think about it. Version 1 of an LLM will look vastly different from version 5, making the ability to hydrate and rehydrate models with contextually appropriate data absolutely table stakes. This requires infrastructure that can maintain perfect chronological integrity while providing real-time access to contextualized event streams. The significance varies dramatically by application: while a customer support inquiry might tolerate some data latency, an anti-money laundering system cannot operate effectively on stale information. Imagine the consequences of an LLM making decisions based on outdated regulatory data or missing recent transaction patterns.
The ability to serve as both a real-time streaming platform and a comprehensive historical record, while maintaining the contextual relationships between events, represents a shift in how we think about data infrastructure for AI. This isn’t about choosing between real-time and historical data; it’s about having both capabilities seamlessly integrated in a way that supports the full lifecycle of AI model development and deployment.
The Architecture Requirements for Layered AI Systems
The emergence of layered workflows, where LLMs feed each other or operate in nested configurations, creates additional architectural demands. These systems require flexible infrastructure that doesn’t necessitate a complete redesign when adding new events, changing topics, or integrating additional data sources. Multi-modal flexibility becomes increasingly important as enterprises seek one management plane with multiple deployment options spanning shared environments, bring-your-own-cloud configurations, or hybrid approaches.
Source system integration capabilities represent another fundamental requirement. Modern AI infrastructure must seamlessly process data from any application, internal or external, across microservices and through industry-standard protocols. This integration challenge is compounded by the need for real-time processing, as LLMs are only as valuable as the integrity and timeliness of their underlying data.
The efficiency demands of real-time AI applications make computational optimization non-negotiable. Organizations cannot afford to indiscriminately accumulate data in platforms like Databricks and expect the system to sort everything out afterward. Instead, infrastructure must be designed from the ground up to support the unique requirements of intelligent systems, balancing performance with cost-effectiveness while maintaining the flexibility to evolve with changing business needs.
The Path Forward
Organizations building AI infrastructure today face a fundamental choice: optimize for current AI workloads from the ground up or attempt to retrofit existing data platforms for requirements they were never designed to handle. Success requires platforms that can serve as immutable sources of truth while providing real-time streaming capabilities, contextual event organization, and the ability to rehydrate historical data for new model requirements.
The infrastructure must support rapid deployment and modification of agent workflows, ideally through intuitive interfaces accessible to business users. Integration with existing systems should leverage current investments rather than requiring wholesale replacements, while data quality and contextualization capabilities must be robust enough to serve as reliable sources of truth across diverse AI applications. Most importantly, the platform must treat every data point as an event that can be organized chronologically, contextualized appropriately, and made available for both real-time inference and historical model training.
The stakes are high. Organizations that recognize this infrastructure imperative today will be positioned to harness AI’s potential, while those that delay these fundamental decisions may find themselves struggling to keep pace with the rapid evolution of intelligent systems.
