Introduction

Early generative models were largely single-modal: language models generated text, vision models generated images, and audio models handled speech or sound. While groundbreaking and still used today, real-world data can appear in many other formats.

Multimodal generative AI refers to models that take inputs from more than one modality and produce outputs in one or more modalities. These models learn representations that relate text, images, video, audio, 3D models, JSON files, code, molecular sequences, and other data types within a shared framework.

That also begs the question of why we don’t employ an Agentic AI with access to various individual specialized generative AI models?

What Is Multimodal Generative AI

Multimodal generative AI is trained to input and output on more than one data modality within a single system, whether that’s text, images, video, audio, 3D models, time-series signals, biological sequences, or a combination of any of them.

A multi-modal generative model:

• Accepts inputs from multiple modalities
• Generates outputs in one or more modalities
• Learns relationships between modalities through a shared representation

Most modern multimodal systems use modality-specific encoders to map inputs into a shared latent space, followed by modality-specific decoders for generation. This allows the model to align information across modalities while preserving the structure of each data type.

Multimodal models allow information from one modality to condition another. For example, a screenshot of an Excel spreadsheet. Visual inputs can guide organized data. Organized data can guide text. Text can then guide language outputs. And language outputs can guide code. This enables workflows that are closer to how domain experts reason about problems.

• Context: combining complementary signals reduces ambiguity
• Expressiveness: outputs can be generated in the form most useful downstream
• Generalization: shared representations transfer across tasks and data types

Practical Constraints

As modalities and model sizes increase, these benefits come with higher computational cost. Training requires larger datasets, more memory, and higher data throughput. The practical value of multi-modal generative AI is therefore tied to both model design and the ability to support it with appropriate compute infrastructure.

Common architectural approaches include:

• Shared latent space models that align modalities through joint embeddings
• Cross-attention models where one modality conditions another explicitly
• Hybrid systems combining transformers, diffusion, and autoregressive components

Multi-modality introduces several practical constraints:

• Data alignment: Paired or temporally aligned datasets are expensive and limited in many domains. Weak alignment reduces model reliability.
• Imbalanced modalities: Dominant modalities can overwhelm others during training, leading to poor cross-modal performance.
• Scaling costs: Each added modality increases model size, memory usage, and training time.
• Evaluation difficulty: Output quality is harder to measure when correctness spans multiple modalities.
• Failure modes: Errors in one modality can propagate into others, producing plausible but incorrect outputs.

What Is Agentic AI

Agentic AI refers to a system in which a central controller—typically a language model—plans actions, selects tools, and invokes one or more specialized generative models to complete a task. This approach treats generative models as callable components within a larger system.

An agentic AI system:

• Routes tasks to specialized single-modal or narrowly multi-modal models
• Coordinates outputs through explicit interfaces and sequential execution
• Makes decisions about tool selection and information flow without learning shared cross-modal representations

These models may be single-modal or narrowly multi-modal, each optimized for a specific function such as text generation, image synthesis, video generation, or protein structure prediction.

Rather than learning a shared representation across modalities, the agent coordinates models through explicit interfaces. Information is passed as text, structured data, or files. The agent’s role is decision-making and sequencing, not joint representation learning.

Practical Constraints

Agentic AI systems are designed for flexibility and modularity, but this comes at a cost. Because they rely on sequential tool invocation and explicit handoffs between models, they face overhead that multimodal systems avoid through joint training. These trade-offs make agentic systems more suitable for tasks where modularity outweigh tight integration.

Common architectural approaches to agentic AI systems include:

• Tool-calling language models: Models that use function calling to invoke external APIs and specialized models
• Thinking Models: Decompose tasks into steps, then execute them sequentially using its access to domain-specific tools
• Retrieval-augmented systems: Agents that query databases, vector stores, or model registries to select appropriate tools dynamically
• Multi-agent frameworks: Distributed systems where multiple agents coordinate, each responsible for a subset of modalities or tasks

While agentic systems are flexible, they introduce several limitations:

• Loss of information: Cross-modal details are often reduced to textual or symbolic summaries when passed between models. Information is compressed or lost when the agent translates context into prompts for each specialized model.
• Error accumulation: Mistakes propagate across steps and are difficult to detect or correct downstream (Multimodal AI suffers from this as well)
• Latency: Multi-step execution increases response time, especially when models run on separate hardware or services.
• Evaluation complexity: Failures can arise from poor Agentic AI planning, tool selection, or model output, which complicates debugging.
• Operational overhead: Maintaining, versioning, and coordinating multiple models increases system complexity.

Agentic systems work best when tasks are decomposable and loosely coupled across modalities. They are less effective when outputs depend on fine-grained, learned relationships between modalities.

From a systems standpoint, agentic AI shifts complexity from training to orchestration. Compute demands are spread across multiple models (or none at all with API calls) and executions, which can simplify (or nullify) training but place greater emphasis on runtime efficiency and scheduling.

Multi-Modal Models vs Agentic AI Systems

Multi-modal generative models and agentic AI systems address similar problems but use different architectural assumptions. The distinction matters in practice.

• Multi-modal generative models learn relationships between modalities during training. Inputs and outputs are mapped through a shared representation, allowing one modality to directly influence another within the model.
• Agentic AI systems coordinate multiple specialized models at inference time. A controller selects models, passes intermediate results, and manages execution flow, but the models remain independent.

In practice, the choice is shaped by data availability, latency requirements, and system constraints. Many production systems combine both approaches: multi-modal models for perception and generation, and agents for orchestration and control.

Multi-modal models are better suited to tasks where modalities are tightly coupled, and outputs depend on fine-grained cross-modal relationships. Examples include text-conditioned video generation or sequence-to-structure prediction.

Agentic systems are better suited to modular workflows, tool use, and cases where modalities interact loosely. They favor adaptability over integration.

Multimodal AI vs Agentic AI FAQ

1. What is the difference between multi-modal AI and agentic AI?

Multi-modal AI learns shared representations across data types within a single model. Agentic AI coordinates multiple specialized models at inference time without shared internal representations.

2. Is agentic AI a replacement for multi-modal models?

No. Agentic systems are better for modular, loosely coupled tasks. Multi-modal models are better when tight cross-modal relationships are required.

3. Are multi-modal models harder to train than single-modal models?

Yes. They require larger datasets, aligned modalities, more memory, and higher compute bandwidth.

4. Which approach has lower inference latency?

Multi-modal models typically have lower latency due to fewer execution steps. Agentic systems incur overhead from orchestration and multiple model calls.

5. Can multi-modal and agentic AI be used together?

Yes. Many systems use multimodal models for perception and generation, with agents handling workflow control and tool selection.

Conclusion

Multi-modal generative models and agentic AI systems represent two different ways to scale generative capabilities across data types. Multi-modal models emphasize integrated learning and tighter cross-modal relationships. Agentic systems emphasize modularity and flexibility through orchestration.

Neither approach is universally better. The right choice depends on how strongly modalities interact, what data is available, and how much latency and system complexity can be tolerated. Maybe a combination of both is ideal, like when ChatGPT uses GPT-4o and GPT-5 to help with reasoning, text generation, coding, and image generation.

From an infrastructure perspective, these choices affect compute layout, memory requirements, data movement, and deployment strategy. As generative systems continue to expand beyond text, architectural decisions at both the model and system level will increasingly determine what is practical to build and operate.