A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications

Advances in large language models have sparked intensive research into creating agentic frameworks that can reason, plan, and act, and the emergence of large multimodal models now enables these agents to process images, audio, and video alongside text. This survey addresses a previously unmet need by systematically examining how multimodality influences each core component of an agentic system—perception, reasoning, planning, memory, and action—tracing the shift from purely text‑based agents to architectures that integrate multiple sensory streams. The authors categorize integration strategies into delegated, late‑fusion, and early‑fusion designs, linking each architectural choice to the capabilities the resulting agents exhibit, such as grounded perception and multimodal reasoning, and they map these designs onto a modality‑centric taxonomy that clarifies how different modalities expand functional reach.

The review further surveys multimodal agentic systems across four major application domains: robotics, graphical‑user‑interface and web navigation, multimedia content generation and editing, and long‑form video understanding and retrieval. By evaluating performance within these settings, the authors highlight concrete trade‑offs between capability gains and the increased computational demands of training and inference, noting issues of latency, scalability, and deployment constraints that accompany richer sensory inputs. Their analysis underscores that while multimodal integration can dramatically improve real‑world applicability, it also introduces efficiency challenges that must be balanced against the desired level of agentic sophistication.

Concluding, the survey identifies critical gaps—such as limited benchmarks for multimodal agency, underexplored early‑fusion techniques, and the need for more robust memory mechanisms that can handle heterogeneous data—and proposes a roadmap toward building robust, general‑purpose intelligent systems. By focusing on the impact of multimodality, the authors aim to guide future research toward agents that can seamlessly combine perception, reasoning, and action across diverse environments, ultimately advancing the field toward more capable and adaptable AI assistants.

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