As machines are becoming more capable of perceiving, understanding, and interacting like humans, multimodal AI agents are taking the center stage in the tech world. By processing and reasoning across text, images, and audio, they make it possible to create applications with more natural and versatile interactions across different industries and functions.
IDC predicts that by 2028, 80% of foundation models used for production-grade use cases will include multimodal AI capabilities to deliver improved use case support, accuracy, depth of insights, and inter-mode context. [1] This means AI agents expand their capabilities by learning to process multiple forms of data at once.
Why are multimodal AI agents in particular driving so much value for businesses? We’ll break it all down — from what they are and their benefits to how they actually work in real-world use cases. Product designers, developers, and business leaders will find effective strategies for getting started implementing multimodal AI agents into their workflows and improving employee and customer experiences.
What are Multimodal AI Agents?
Figure 1: Multimodal AI Agents
Multimodal AI agents are intelligent systems that can act and reason across diverse types of data simultaneously. They use Natural Language Understanding (NLU) for text, Computer Vision for images and video, and Speech Recognition and Synthesis (Text-to-Speech) for audio. Such integration allows them to accomplish more complex tasks, which require a holistic understanding of the environment and the user’s intent.
Simple AI agents specialize in only one modality, for example an AI agent that operates via text over webchat. Multimodal AI agents, in contrast, can perform tasks such as understanding a user’s spoken question over voice, processing an image accompanying the query, and responding through synthetic speech in a single coherent interaction. This makes them much more flexible and human-like in terms of interface design and performance, enabling them to handle more complex tasks.
The Evolution of Multimodal AI Agents: From Chatbots to Foundation Models That are Natively Multimodal
The transition to multimodal AI agents was expected, as multimodality has always been an important design consideration for creating robust, human-like automated user experiences.
The modern era of voice assistants began with the launch of Siri, first voice assistant to reach a wide audience [2], and it has continued throughout the years. Recent breakthroughs came with the advent of large language models (LLMs) that are natively multimodal, such as OpenAI’s GPT-4o, Google’s Gemini, and Meta’s CM3leon. These LLMs use collective processing and are able to generate outputs across multiple modalities, creating intelligent, human-like experiences.
Using strong transformer architectures and cutting-edge training techniques, such as the Pathways system (used in Google’s PaLI (Pathways Language and Image) model), these LMMs are capable of scaling learning across diverse text, image, and audio datasets. The main benefit of this unified model pipeline is that an AI agent integrates sensory inputs and contextual reasoning to act smoothly and intelligently, much like humans.
Discover the best practices for implementing AI agents at scale
Download the Strategy GuideKey Components of Multimodal AI Agents
Figure 2: Key Components of Multimodal AI Agents
- Natural Language Understanding (NLU): An agent understands user queries and natural language commands.
- Computer Vision: An agent understands visual inputs coming from images and videos, recognizing objects, scenes, and even gestures.
- Speech Recognition & Text-to-Speech (TTS): An agent converts spoken words into written text and generates natural-sounding speech, enabling smooth voice interactions.
- Contextual Reasoning: An agent collects information from text, images, and audio to understand the broader context and generates more contextually relevant and accurate responses.
Advantages of Multimodal AI Agents
Figure 3: Unimodal AI Agents vs. Multimodal AI Agents
This versatility makes multimodal AI agents ideally suited to tackle complex, real-world situations in which the information available is inherently multimodal, such as reading a patient’s medical records along with diagnostic images and spoken symptoms.
Multimodal AI Agent Use Cases
By industry
- Healthcare: Multimodal AI agents can simultaneously analyze images of medical X-rays and MRIs, patient records, and doctor-patient interactions to support diagnosis and personalized treatment options. This holistic approach enhances both the accuracy of diagnosis and the treatment of patients.
- Retail and E-Commerce: By integrating computer vision and language models with speech-enabled AI agents, multimodal agents enable customers to describe products verbally, upload images, and even show gestures, and obtain accurate recommendations for the product.
- Automotive: By combining visual perception, audio cues, and textual navigation instructions, multimodal agents increase situational awareness and decision-making in self-driving cars.
By function
- Product Development: Research, prototyping, and decision-making are accelerated with multimodal AI agents at all stages of the product development process, as they process diverse data, such as text, diagrams, tables, and images, simultaneously. In fields such as pharmaceuticals and engineering, this boosts innovation and shortens time-to-market. They’re also valuable for product teams to quickly gather and analyze customer feedback, as well as to automate documentation updates using information from reports and visuals.
- Customer Support: Multimodal AI agents offer enhanced support by interpreting images, screenshots, and text to address issues in a quick, accurate, and comprehensive way. They can detect sentiment, prioritize urgent cases, and deliver personalized responses. AI agents offer 24×7 multichannel support by combining text, voice, and visuals, while reducing operational costs.
- Employee Experience: With multimodal AI agents, employees can quickly access information using natural language and visual search. Routine HR and IT tasks, such as onboarding and troubleshooting, can be automated, freeing up time for high-value work.
According to Slack’s report AI Agents Are the Catalyst for a Limitless Workforce, people working with agents are 72% more likely to say they feel “very productive” at their jobs. And the alternative is costly — workers who don’t use agents spend nearly 40% more time on administrative tasks compared to those who do. [3]
Designing Human-Like AI Interfaces with Multimodal AI Agents
When combined, these multiple modalities make the AI interfaces more natural and intuitive. For instance, a speech-enabled AI agent can engage in a conversation with the user and, simultaneously, interpret visual cues or documents displayed to it during the dialogue. As in real-life conversation, where we combine speech with gesture and visual context, this provides a seamless communication experience.
AI agents with text-to-image AI capabilities can generate or interpret images from text, enhancing conversations with visual content. AI interaction design focuses on multimodal experiences, emphasizing fluid transitions between modalities and contextual awareness to reduce friction and improve user satisfaction.
Challenges in Implementing Multimodal AI Agents
Multimodal AI agents combine various types of data, including text, images, audio, and video, to deliver richer, more human-like interactions. Yet creating and using these agents comes with some challenges:
- Data Integration Complexity: Merging diverse data sources requires complex architectures and massive amounts of training data. Ensuring these different modalities work together seamlessly is a technical must.
- Real-Time Processing Demands: To provide smooth, natural interactions, multimodal agents must process and synchronize various inputs quickly. Achieving low latency while handling complex data streams puts significant strain on computational resources.
- Contextual Understanding: Human communication is often subtle and nuanced, involving tone, body language, and context. Teaching AI agents to interpret these layers across multiple modalities requires advanced reasoning and common-sense knowledge.
- Ethical and Privacy Concerns: Handling varied types of data also emphasizes important ethical questions regarding privacy, bias, and transparency. Protecting user data and ensuring fairness becomes more complicated when integrating diverse sources.
To address these challenges, OneReach.ai’s Generative Studio X (GSX), an Agent platform, offers a powerful toolset for designing, training, and orchestrating multimodal AI agents. The GSX platform enables IT and business leaders to seamlessly integrate multiple data modalities, manage real-time processing demands, and build AI agents that are contextually aware and ethically designed.
The Power of Multimodal AI Agents with Agency
Multimodal AI agents are transforming the way we interact with technology, combining text, vision, and speech to create more natural and intuitive interactions. What makes this truly groundbreaking is when multimodality is paired with agency — the ability to act independently toward a goal. Not only do these systems “comprehend” diverse inputs, but they also “do” things based on them.
As technology futurist Bernard Marr puts it: “The businesses leading their industries in the next decade will be those that deploy these multimodal agents effectively… augmenting our capabilities with systems that can process and act on information in ways we simply cannot match in scale or speed.”
For organizations, adopting multimodal AI agents is quickly becoming a strategic must to keep pace in a world where intelligent, context-aware interaction is the new standard.
Want to drive operational efficiency and productivity with AI agents?
Book a DemoRelated Questions About Multimodal AI Agents
1. What is a multimodal AI agent vs. a unimodal AI agent?
Unimodal agents process only one type of data (e.g., text or images), while multimodal AI agents integrate multiple data types, text, vision, and speech, to better understand and respond appropriately
2. What is the nature of computer vision and language models?
Vision and language models are trained for vision-text alignment, enabling them to perform tasks such as captioning, visual question answering, and cross-modal retrieval.
3. What are the advantages of speech-enabled AI agents?
Speech-enabled AI agents are available 24×7, provide personalized responses, and can manage high volumes of conversations at the same time, improving customer service and operational efficiency.
4. What technologies are necessary for building multimodal AI agents?
Key technologies include Natural Language Understanding (NLU), Computer Vision, Automatic Speech Recognition (ASR), and Text-to-Speech (TTS), all integrated into a unified agent framework.
5. How can enterprises get started with implementing multimodal AI agents?
Begin by identifying high-impact use cases, choosing appropriate models and frameworks, designing intuitive user experiences, and continuously improve your agents based on real-world feedback.
