Any-to-Any: Building Native Multimodal Agents - Patrick Löber, Google DeepMind

Building native multimodal agents with the Gemini API enables any-to-any capabilities—understanding multiple input modalities (text, code, image, audio, vide...

By Sean Weldon

Abstract

This paper examines the architecture and implementation of native multimodal agents using Google's Gemini API, which enables comprehensive any-to-any processing capabilities across text, code, image, audio, and video modalities. The approach employs an agentic architecture where Gemini serves as a reasoning engine that dynamically selects specialized generation models through function calling, rather than relying on predetermined workflows. Key technical contributions include efficient token-based processing supporting over nine hours of audio content, context caching mechanisms reducing costs by 90%, and unified audio-to-audio architectures for real-time interactions. A reference implementation demonstrates practical application through a Notebook LM clone that autonomously generates multimodal study materials. These findings have significant implications for building adaptive AI systems capable of reasoning about optimal output modalities based on content characteristics, representing a paradigm shift from hardcoded pipelines to intelligent, context-aware generation strategies.

1. Introduction

The evolution of artificial intelligence systems has progressed from single-modality processing toward comprehensive multimodal understanding and generation. While many contemporary systems achieve multimodal capabilities through cascaded pipelines—chaining specialized models sequentially—this approach introduces latency, error propagation, and architectural complexity. The emergence of native multimodal models presents an alternative paradigm where unified architectures process diverse input types and coordinate specialized generation capabilities through intelligent reasoning rather than predetermined sequences.

This synthesis examines the technical architecture and implementation patterns for building multimodal agents using the Gemini API ecosystem. The central thesis posits that effective multimodal agents require not merely the ability to process multiple input types, but an agentic reasoning layer capable of determining which output modalities best serve user needs. As articulated in the source material, "We want to build this as an agent rather than a workflow. This means that the agent should be able to decide what to create rather than where we hard code the pipeline."

The analysis proceeds through four primary dimensions: multimodal understanding capabilities and their technical constraints, native generation across image and audio modalities, agentic architectures employing function calling, and real-time interaction patterns. A concrete implementation case study—a Notebook LM clone—demonstrates these principles in an educational context, illustrating how an agent can autonomously decide whether to generate images, speech, or infographics based on content complexity.

2. Background and Related Work

Multimodal AI systems traditionally employ one of two architectural approaches: early fusion, where inputs are combined before processing, or late fusion, where separate models process each modality before integration. The Gemini architecture represents a third paradigm—native multimodal processing where a single model directly interprets multiple input types without intermediate conversion steps. The Gemini 3 model natively understands text, code, image, audio, video, URLs, and Google Search inputs, providing a unified processing foundation.

The concept of agentic AI builds upon function calling and tool use frameworks, where language models reason about which external capabilities to invoke. Unlike reactive systems that execute predetermined sequences, agents employ iterative reasoning loops to assess task requirements and select appropriate tools dynamically. The current Gemini architecture implements this through a hybrid approach: Gemini 3 serves as the main reasoning model with text-only output, while specialized native generation models—including Nano Banana 2 for image generation and Gemini 2.5-based text-to-speech models—handle specific output modalities. The vision articulated is to consolidate more generation capabilities into a single Gemini model over time, though the current distributed architecture enables specialized optimization.

The Gemma model family complements cloud-based Gemini capabilities by enabling local multimodal understanding with smaller variants, including Gemma 4 with native audio support. This distributed ecosystem approach balances computational efficiency with comprehensive capability coverage across deployment contexts.

3. Core Analysis

3.1 Multimodal Understanding Architecture and Token Economics

The Gemini API implements multimodal understanding through a unified token-based processing model with specific computational constraints that inform practical application design. Audio processing operates at a rate of 1,920 tokens per minute, enabling Gemini's 1 million token context window to support over nine hours of audio content within a single API call. Video processing operates under similar token constraints, supporting approximately one hour of video content within the token limit.

The implementation pattern demonstrates remarkable simplicity: developers upload files using the Google AI SDK and invoke processing through a single client.models.generate_content() API call, regardless of input modality. This abstraction masks substantial underlying complexity while providing a consistent developer experience across text, PDFs, videos, and MP3 files. API access is available without cost at ai.google.dev, with SDKs supporting multiple programming languages.

Context caching represents a critical optimization for multimodal processing economics. Built directly into the API, this mechanism stores intermediate representations of processed content, reducing costs by 90% for repeated queries on the same files. This optimization proves especially valuable for applications involving lengthy video or audio analysis where users may pose multiple questions about the same source material.

The architecture additionally supports direct YouTube URL processing with timestamp range specification, enabling targeted analysis of specific video segments. This cross-modal understanding capability allows the system to draw connections across different source types simultaneously—for example, synthesizing information from PDFs, videos, and audio recordings in a unified response.

3.2 Native Generation Capabilities: Image and Audio Synthesis

The concept of native generation models distinguishes Gemini's approach from conventional multimodal pipelines. As explained in the source material, "These models are 'native' because they are based on Gemini. So all the training or a lot of the training that goes into the main Gemini models are now also available in these models." This shared training foundation provides what is termed "world understanding"—contextual knowledge that enables more sophisticated generation than isolated specialist models.

The Nano Banana 2 model, accessed via the Gemini 3.5 flash image preview endpoint, demonstrates this native generation capability through several key behaviors. The model can interpret visual annotations such as arrows on maps and generate contextually appropriate images—for instance, correctly producing an image of the Golden Gate Bridge when given a map with an arrow pointing to its location. This capability extends to understanding mathematical concepts, enabling the system to correct homework with visual explanations, and generating code overlaid on images or educational infographics from text prompts.

Native speech generation operates through a Gemini 2.5-based text-to-speech model with configurable parameters supporting multilingual output, accent control, and tone specification. The architecture supports two-speaker audio files for podcast-style generation, enabling applications like automated study material conversion to conversational audio formats. Notably, the Gemini 3 flash and Flashlight models additionally provide transcription capability when prompted appropriately, demonstrating bidirectional audio processing.

3.3 Agentic Architecture Through Function Calling

The agentic architecture employs Gemini as a reasoning engine that determines which specialized models to invoke through a function calling mechanism. This approach requires three components: function declarations with names, descriptions that help the model understand each tool's purpose, and parameter definitions specifying required inputs. Developers configure tools in model calls and add function descriptions to the system prompt, enabling the agent to reason about tool selection.

The agent operates in an iterative reasoning loop, continuously evaluating whether generated assets sufficiently address user needs or whether additional outputs in different modalities would provide value. In the educational context, for example, the agent analyzes study material and autonomously decides which concepts require visual diagrams versus audio summaries, rather than following a predetermined generation sequence.

This architectural pattern proves transferable across domains beyond education. The fundamental mechanism—a reasoning model that selects from a palette of specialized generation capabilities—applies to any context where optimal output modality depends on content characteristics and user requirements. The Gemini API Skill provides additional abstraction over implementation details for agent builders, further reducing development complexity.

3.4 Real-Time Interaction Patterns

The Gemini 3.1 flash live model introduces a distinct architectural pattern for real-time interactions through a unified audio-to-audio architecture. Unlike cascaded pipelines that chain speech-to-text, language processing, and text-to-speech models sequentially, this single unified architecture processes audio input and directly produces audio output. This design choice substantially reduces latency and enables more natural-sounding interactions compared to multi-model pipelines where each stage introduces processing delay and potential error propagation.

The model is accessible for testing at ai.google.dev/live, enabling developers to experience the low-latency, natural conversation capabilities directly. This real-time capability represents a complementary pattern to the batch-oriented multimodal understanding and generation workflows, addressing use cases requiring immediate interactive responses rather than comprehensive content analysis and synthesis.

4. Technical Insights

Several technical considerations emerge as critical for implementing native multimodal agents effectively. The token economics of multimodal processing demand careful attention: at 1,920 tokens per minute of audio, developers must architect applications with awareness of context window constraints. For video processing supporting approximately one hour within token limits, applications requiring longer content analysis should implement segmentation strategies or leverage context caching to manage costs.

The function calling architecture requires precise function declarations where description quality directly impacts agent reasoning effectiveness. Developers must craft descriptions that clearly communicate each tool's purpose and appropriate use cases, as the reasoning model relies on these descriptions to make selection decisions. Parameter schemas should be designed with appropriate constraints to prevent invalid tool invocations.

Model selection represents another implementation consideration. The Gemini 3 flash and Flashlight models provide transcription capabilities when prompted, but developers must explicitly request transcription in prompts rather than assuming automatic activation. For image generation, accessing Nano Banana 2 requires specifying the Gemini 3.5 flash image preview endpoint rather than standard Gemini endpoints.

The Gemma model family offers an alternative deployment pattern for scenarios requiring local processing without cloud API dependencies. Gemma 4 particularly enables local multimodal understanding with native audio support, though with reduced capabilities compared to cloud-based Gemini models. This trade-off between capability and deployment flexibility requires evaluation based on specific application requirements around data privacy, latency, and offline operation needs.

5. Discussion

The native multimodal agent architecture examined here represents a significant departure from conventional multimodal system design. Rather than viewing multimodal capability as a pipeline engineering challenge—connecting specialized models through carefully orchestrated data transformations—this approach frames it as a reasoning problem where an intelligent agent selects appropriate generation modalities based on content analysis. This paradigm shift has substantial implications for system adaptability and maintenance, as adding new generation capabilities requires updating the agent's tool palette rather than redesigning entire pipelines.

The economic implications of context caching deserve particular attention. The 90% cost reduction for repeated queries on cached content fundamentally alters the economics of multimodal analysis applications. Systems that previously required cost-prohibitive reprocessing for each user query can now support interactive exploration of large multimodal datasets at viable price points. This optimization may enable entirely new application categories where users iteratively refine understanding through conversational interaction with multimodal content.

Several areas warrant further investigation. The current architecture's reliance on Gemini 3 for reasoning with text-only output, while practical, introduces a potential bottleneck where the reasoning model cannot directly incorporate visual or audio information when making generation decisions. The stated vision of consolidating more generation capabilities into a single Gemini model suggests this limitation may be temporary, but the implications for agent reasoning quality remain unclear. Additionally, the trade-offs between unified audio-to-audio models like Gemini 3.1 flash live versus cascaded pipelines merit systematic evaluation across latency, quality, and controllability dimensions.

The transferability of the agentic pattern across domains beyond the demonstrated educational use case requires empirical validation. While the fundamental mechanism appears domain-agnostic, the effectiveness of agent reasoning in selecting appropriate output modalities likely depends on domain-specific content characteristics and user expectation patterns that may require specialized tuning.

6. Conclusion

This analysis has examined the architecture and implementation patterns for native multimodal agents using the Gemini API ecosystem. The key contribution lies in demonstrating how agentic reasoning over specialized generation models enables dynamic, context-aware multimodal output selection rather than predetermined workflows. Technical findings include efficient token-based processing supporting over nine hours of audio content, context caching reducing costs by 90%, and unified architectures for real-time audio interactions.

The practical implications extend beyond technical architecture to fundamental questions about how AI systems should approach multimodal generation. Rather than requiring developers to anticipate all possible output modality combinations and hardcode appropriate pipelines, the agentic approach enables systems to adapt generation strategies based on content analysis. This flexibility proves particularly valuable in domains like education, where optimal presentation format depends on concept complexity and learning objectives.

For practitioners, the immediate takeaway involves recognizing that effective multimodal systems require both comprehensive input understanding and intelligent output reasoning. The Gemini API ecosystem provides the technical foundation for such systems through native multimodal processing, specialized generation models, and function calling mechanisms. Future development should focus on refining agent reasoning capabilities, expanding the palette of available generation modalities, and systematically evaluating the approach across diverse application domains to establish best practices for this emerging architectural pattern.

Sources

About the Author

Sean Weldon is an AI engineer and systems architect specializing in autonomous systems, agentic workflows, and applied machine learning. He builds production AI systems that automate complex business operations.

LinkedIn | Website | GitHub