AI Era Data Infrastructure: From Storing Data to Enabling Agent‑Driven Context

Data as Cognitive Fuel

Historically, data platforms focused on reliably storing data and delivering query results, reports, or predictions. With AI agents that can understand intent, decompose tasks, and act autonomously, data’s role shifts from a passive record to the "cognitive fuel" that supplies context, judgment, and continuous learning for agents.

New Mission for the Data Foundation

The data foundation must now guarantee that every round of intelligent decision‑making receives traceable, explainable, and instantly callable context. This context goes beyond tabular facts to include enterprise knowledge bases, AI assets, agent memory, and execution traces, all organized into a unified context layer.

AI‑Native Data Platform: Co‑evolution of Three Layers

Storage layer : Moves from merely persisting structured facts to a unified context plane that stores multimodal data, knowledge, agent memories, and feedback, enabling continuous retrieval and learning.

Compute layer : Expands from predefined precise calculations to a mix of retrieval, reasoning, code execution, and context construction with low latency, integrating structured SQL, multimodal processing, model training, and inference.

Application layer : Transitions from human‑oriented query results to high‑trust, self‑optimizing agent applications that can autonomously complete complex tasks, requiring the platform itself to exhibit "Agentic" capabilities.

From "Store‑Well, Compute‑Well" to Continuous Evolution

The data lake must upgrade in three dimensions: a trustworthy context base, a new compute paradigm, and Agentic capabilities. The goal is to let the lake serve as the continuous source of reliable context for AI agents.

Concrete Architecture (AI DLC)

The proposed architecture builds on the TCLake AI Lakehouse and includes:

TCCatalog – a unified multimodal data catalog.

TCIceberg – structured data storage.

Lance – multimodal lakehouse storage.

Meson – Spark‑compatible high‑performance compute engine.

Xpark – custom multimodal data processing with datasets, dataframes, AI operators, and ML/LLM functions.

TCRay – enhanced Ray kernel covering Ray Core, Ray Data, Ray Train, Ray Serve, object storage optimization, fault‑tolerant checkpointing, and heterogeneous acceleration.

These components are exposed via Jupyter, VS Code, Web Shell, CLI, SDK, and REST APIs.

Agentic Services

TCDataAgent‑EVA provides data‑analysis, data‑engineering, data‑science, and intelligent‑search agents; TCInsight handles compute and storage optimization. Higher‑level agents (WorkBuddy, content‑creation, intelligent‑marketing, autonomous‑driving, game‑operations, etc.) can be built on top, forming four capability categories: storage intelligence, compute intelligence, system intelligence, and data intelligence.

Expected Benefits

The upgrades aim to shorten the value‑realization cycle from data to intelligence, reduce system integration and context‑drift overhead, and make expensive compute resources continuously productive. The focus shifts from expanding storage or single‑run performance to creating a persistent, trustworthy context pipeline that supports ongoing AI decision‑making and self‑optimization.

Conclusion

When agents evolve from mere query tools to autonomous users that continuously perceive the real world, the design of data platforms must also evolve. A unified context, multimodal compute, and Agentic capabilities together ensure that data can be reliably invoked in every intelligent decision loop and that the outcomes feed back as the next round’s context.