The transition from text-only agents to systems capable of processing images, audio, and video is no longer an isolated experiment: a systematic framework now documents its foundations and current boundaries. The paper "A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications," authored by Neel Mokaria and 15 others, has been accepted for publication in TMLR (Transactions on Machine Learning Research), marking a consolidated reference point for the scientific community.

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A Modality-Centric Taxonomy

Unlike previous reviews that focused on generic architectures or non-systematic approaches, this study adopts a modality-centric perspective. The goal is to analyze how different sensory modalities are integrated into the five fundamental modules of an agentic framework: perception, reasoning, planning, memory, and action. This structure allows isolating specific bottlenecks that emerge when a model must handle heterogeneous information flows simultaneously, going beyond the simple sum of unimodal capabilities.

The Role of Large Multimodal Models

The work highlights how the advent of Large Multimodal Models (LMMs) is redefining the limits of agency. Modern systems do not merely respond to textual prompts but orchestrate perception and decision-making around powerful LLM backbones, integrating real-time sensory data. This approach improves real-world applicability, where interactions are rarely purely textual. The survey fits into a context of rapid evolution, where models like DeepSeek V4-Flash Vision and Fara1.5-27B have already demonstrated the feasibility of agents that navigate graphical interfaces based solely on screenshots, without DOM code access.

Implications for Infrastructure and Research

The systematic nature of the review provides a solid foundation for those developing open-source or commercial agentic frameworks. By identifying current techniques and applications, the paper highlights open challenges related to scalability and evaluation metrics. For IT professionals, this means that AI agent design can no longer ignore multimodal memory architecture: the ability to maintain coherence over long sequences of sensory input becomes a critical factor for operational reliability, as already highlighted by research on drift in multi-turn reasoning.