Agentic AI Needs an Omnimodal Data Platform

AI agents are revolutionizing how organizations interact with their data. But as companies rush to implement agentic AI, they're discovering a harsh truth: their fragmented data infrastructure makes building effective, secure, and scalable AI agents extraordinarily difficult.

The path forward requires a fundamental shift in how we think about data management. Agentic AI doesn't just need better agents, it needs an omnimodal data platform.

What Is an AI Agent?

To understand why agentic AI requires omnimodal intelligence, let's start with the basics.

Large language models (LLMs) like ChatGPT and Claude are powerful, but they have a critical limitation: they can only answer questions probabilistically based on their training data. If you're a doctor asking ChatGPT about your patient "Stavros," the LLM has no idea what you're talking about because ChatGPT wasn't trained on your hospital's patient database.

You might consider fine-tuning an LLM on your private data, but this opens a huge can of worms. Fine-tuning is expensive and must be repeated as new data accumulates, making it economically impractical. Worse, once private data enters LLM weights, anyone accessing the LLM can potentially discover sensitive information.

AI agents solve this problem. An agent combines an LLM with tools, giving it capabilities to access private data infrastructure, query databases, search filesystems, execute analyses, and more. When you ask a question, the LLM dissects it, determines which tools to use, accesses the relevant private data, and generates an answer informed by that data.

With agentic AI, you augment the LLM with your private data economically and in a way that preserves privacy. Organizations can define proper access policies so each employee can only retrieve data they're authorized to see.

This is the promise. But the reality is far more complex.

The Challenges of Agentic AI in Today's Fragmented Infrastructure

Building a single AI agent for one data source is challenging but manageable. Databricks can build agents for querying tables. A specialized vendor like Kepler can build agents for single-cell data. But modern organizations need something far more sophisticated: multi-agentic AI that uses multiple agents to access diverse data types in response to a user's question.

This is where fragmented data infrastructure brings significant challenges.

Challenge 1: Authentication Chaos

In a multi-agentic system, a user must authenticate themselves with every single agent, using different credentials for each underlying system. Postgres requires one set of credentials. Snowflake requires another. The genomics analysis platform requires yet another. The imaging system needs its own authentication.

This isn't just inconvenient, it's a security liability. Each authentication point is a potential vulnerability. Users struggle with credential sprawl, and IT teams face an administrative burden managing access across disparate systems.

Challenge 2: Catalog Fragmentation

Each agent must implement cataloging functionality so it can find the data the user is asking for. For tabular data in a single system like Snowflake, this might be manageable. But what about files scattered across object stores? Single-cell experiments in specialized databases? Genomic variants in VCF format? Imaging data in proprietary formats? Point clouds for 3-D analysis?

Each data type requires its own cataloging approach. Without a unified catalog, agents can't effectively discover relevant data across the organization. Users must know exactly which agent to call for which data type, eliminating the "just a prompt" simplicity that makes AI agents valuable in the first place.

Challenge 3: Governance Complexity

Agents themselves are modalities that require governance. There must be a layer determining which users can see which agents and which users have access to which agents. Without this, organizations must essentially re-invent the entire data management wheel for agents and add more software vendors to an already complicated infrastructure.

Each agent vendor implements its own approach to access control. Some users need access to certain agents but not others. Some agents should only work with specific data sources for specific users. Auditing becomes nearly impossible when activity is logged separately by each agent and data source.

Challenge 4: The "Unstructured" Data Problem

For unstructured data, which is the vast majority of enterprise data, the situation is even worse.

First, giving filesystem access to an agent for retrieving arbitrary files is meaningless for the LLM, since it won't understand what to retrieve. Files appear as blobs without context. Without proper metadata and indexing, such a "filesystem agent" would be futile.

Second, certain data types are extremely large, complex, and have sophisticated semantics that are difficult to handle at scale. Specifically, when genomics, transcriptomics, medical imaging, and point clouds are stored as unstructured blobs in object stores, no agent can resolve the performance and scaling issues, rendering their use completely impractical.

Organizations can't fully leverage AI's capabilities, not because of the quality of LLMs and agents, but because their data infrastructure quality prevents them from building agents that efficiently and economically access all organizational data.

Organizations don't have an AI strategy problem. They have a serious data management problem. They've had this problem before LLMs, even when humans manually searched data sources for insights. As long as organizations treat agentic AI as the silver bullet, categorize non-tabular data as unstructured, and maintain an extremely fragmented data architecture, they'll continue to fall short when realizing the maximum value of AI.

How an Omnimodal Platform Fixes This

An omnimodal data platform solves the fundamental challenges of agentic AI by treating all data and the agents themselves as modalities within a unified system.

Single unified authentication, access control, and logging: Users authenticate once with the omnimodal platform. The platform manages access control across all data types and all agents. All activity is logged in a central system for auditing and compliance. This isn't just more convenient, it's more secure, more manageable, and more compliant with regulatory requirements.

Unified catalog for true discoverability: With all data treated as modalities with semantic context and metadata, a single catalog spans the entire organization. Agents can discover relevant data across tables, files, genomics, imaging, and any other data type through keyword search, domain-specific filters, and sophisticated indexing. Users can truly "just prompt" without knowing which specific agent or data source to target.

Agents as governed modalities: Agents themselves are modalities within the platform, subject to the same governance framework as data. The platform determines which users can access which agents, logs all agent activity, and ensures secure, compliant operation. Building and deploying new agents becomes straightforward, where developers can focus exclusively on the agent logic, while the entire data management and governance platform is already in place, with no need to reinvent the wheel.

Handling all data types effectively: An omnimodal platform must handle tabular data (through integrations with Postgres, Snowflake, and Databricks, for example), approve non-tabular formats (through virtual filesystems with secure tool usage), and disapprove formats (through conversion to efficient representations like arrays). This comprehensive approach ensures agents can access all organizational data performantly and economically.

How TileDB Carrara Implements Omnimodal Intelligence for Agentic AI

TileDB Carrara was built from the ground up to provide the omnimodal foundation that agentic AI requires.

Everything is a modality: Tables, files, genomic data, imaging data, code, dashboards, and AI agents themselves all are modalities in Carrara with semantic metadata and governance.

Comprehensive data handling:

• Tabular data: Carrara integrates with Postgres, Snowflake, Databricks, and other systems, treating external tables as modalities

• Approved non-tabular formats: Carrara's virtual filesystem welcomes formats like TIFF, VCF, h5ad, LAS/LAZ, and countless others, allowing users to continue using their favorite domain-specific tools while operating within Carrara's secure computational framework

• Disapproved formats: For data with performance and scale limitations, Carrara converts to TileDB arrays, which are efficient representations optimized for large-scale access while preserving full semantic meaning

Unified authentication, catalog, access control, and logging: Carrara provides a single authentication system, comprehensive catalog spanning all modalities, fine-grained access control, and complete activity logging. Information security teams approve Carrara knowing it ensures security compliance across the entire offering.

Focus on building agents, not infrastructure: With Carrara, developers focus exclusively on building agent logic. They don't worry about authentication across multiple systems, building catalog functionality, implementing access control, or optimizing data access. The platform handles all of this.

Building agentic and multi-agentic AI in Carrara: Users can build agents as notebooks or more sophisticated implementations within Carrara. For multi-agentic AI, the LLM can orchestrate multiple agents, each accessing different modalities, all within Carrara's unified governance framework. Users authenticate once, agents discover data through the unified catalog, and all activity is logged centrally.

Here's a concrete example: Imagine a pharmaceutical researcher asking, "What genetic variants are associated with treatment resistance in our recent clinical trial, and how do they correlate with imaging biomarkers?"

In a fragmented infrastructure, this question is a nightmare:

• One agent queries a clinical trial database (authenticate with Postgres)

• Another agent searches for relevant genomic variant files (authenticate with object storage and hope there's useful metadata)

• A third agent analyzes imaging data (authenticate with imaging system)

• Somehow orchestrate these agents to work together

• Ensure the user has appropriate access to all relevant data

• Log activity across multiple systems for compliance

In Carrara with omnimodal intelligence:

• User authenticates once with Carrara

• A multi-agentic AI system leverages the unified catalog to discover relevant clinical trial records, genomic variants (as modalities with semantic metadata), and imaging data (as modalities with visualization capabilities)

• Multiple agents work within Carrara's computational framework to analyze data from diverse sources

• Access control is enforced consistently across all data types

• All activity is logged centrally

• The LLM synthesizes insights from all sources

The difference is transformative.

The Future Is Omnimodal

The unified architecture of TileDB's omnimodal platform makes it ideal for the agentic AI revolution. We've recently partnered with leading hyperscalers AWS and Microsoft, technology providers Snowflake and Databricks, and global systems integrators EPAM, Cognizant, and Accenture to demonstrate how omnimodal intelligence unlocks agentic AI's full potential.

But the future of omnimodal intelligence extends beyond TileDB. With features like modality builders and AI agent builders coming soon, customers and partners will play crucial roles in this digital transformation. Organizations can create custom modalities for their unique data types. Partners can build specialized agents for domain-specific tasks. Developers can focus on creating value rather than wrestling with infrastructure.

This is the paradigm shift that agentic AI demands.

The choice facing organizations today is stark: continue struggling with authentication chaos, catalog fragmentation, governance complexity, and unstructured data limitations — or embrace omnimodal intelligence and build agentic AI the way it was meant to be built.

The future of AI isn't just about smarter models or more sophisticated agents. It's about building the omnimodal data foundation that can truly support them. Organizations that recognize this need and invest in omnimodal architecture today will be the ones leading tomorrow's AI revolution.

Ready to discover how omnimodal intelligence can transform your agentic AI strategy? Contact us to learn how TileDB Carrara can revolutionize your approach to AI agents and unlock the full potential of all of your organization’s data.