VideoAgent: Multi-Agent Video Intelligence

Updated 1 September 2025

VideoAgent is a suite of agent-based frameworks designed for robust multi-modal video understanding, processing, and generation through distributed and modular paradigms.
It integrates dynamic memory constructs, chain-of-thought reasoning, and zero-shot tool use to enable precise video trimming, summarization, and real-time decision-making.
Empirical benchmarks show that VideoAgent systems achieve significant improvements in coverage, accuracy, and resource efficiency across various video processing tasks.

VideoAgent encompasses a family of agent-based frameworks, algorithms, and system paradigms designed for efficient, robust, and intelligent multi-modal video understanding, processing, and generation. The term spans landmark work in distributed multi-agent fast-forwarding (Lan et al., 2020, Lan et al., 2023), agent-driven video trimming and summarization (Yang et al., 12 Dec 2024), zero-shot multi-modal video question answering (Wang et al., 15 Mar 2024, Fan et al., 18 Mar 2024, Zhi et al., 6 Apr 2025, Kugo et al., 25 Apr 2025, Montes et al., 21 May 2025), curiosity-driven and chain-of-shot reasoning models (Yang et al., 12 Dec 2024, Wang et al., 6 Jun 2025), multi-agent and modular architectures leveraging vision-LLMs (VLMs/LLMs) (Lan et al., 2023, Fan et al., 18 Mar 2024, Kugo et al., 25 Apr 2025), as well as agents for video generation, story synthesis, or robotics-oriented visual planning (Wang et al., 12 Mar 2024, Soni et al., 14 Oct 2024, Hu et al., 7 Nov 2024). Common to these systems is the delegation of complex perceptual, cognitive, reasoning, or content creation tasks among one or more agents—each equipped with specialised tools, memory constructs, and interactive decision-making capabilities.

1. Key Agent-Based Paradigms and Frameworks

Across the literature, VideoAgent refers to either single-agent or multi-agent systems, with notable paradigms including:

Distributed Multi-Agent Video Fast-Forwarding (DMVF): Cameras paired with RL agents collaboratively determine frame selection and transmission rates through consensus-based evaluation of frame importance. Strategies (e.g., normal, slow, fast) are adapted according to local and network-wide coverage rankings and resource constraints (Lan et al., 2020, Lan et al., 2023).
Centralized Collaborative Frameworks (MFFNet): Agents relay buffered fast-forwarded clips to a central controller that assigns strategies using optimization over inter-view similarities, enabling system-wide control and main-view selection (Lan et al., 2023).
Modular and Tool-Enhanced Agent Systems: Multimodal agents bridge LLMs and VLMs with zero-shot tool-use for segment localization, object memory querying, and visual question answering. These agents employ structured memory (temporal, object-centric), SQL-like querying, and iterative decision loops (Fan et al., 18 Mar 2024, Fan et al., 31 Dec 2024).
Chain-of-Thought and Planning Agents: Agents iteratively sample, reason, self-reflect, estimate uncertainty, and plan information acquisition (frames, segments, questions) to answer complex queries while mitigating noise from external tools by self-assessing reliability (Wang et al., 15 Mar 2024, Zhi et al., 6 Apr 2025).
Multi-Agent Cooperative VQA and Reasoning: Specialized agents for scene graph analysis, vision, and text jointly collaborate, with an Organizer Agent synthesizing multimodal outputs for robust question answering in video settings (Kugo et al., 25 Apr 2025).
Reward-Driven Multi-Agent Reasoning: Integration of real-time reward generation guides iterative prediction refinement using multi-perspective reflection, with automatic selection of high-quality data for further policy optimization (Zhou et al., 2 Jun 2025).
Agent-Driven Video Generation: Agents orchestrate story-to-video pipelines using evolutionary RAG, utility layers, and cross-agent collaboration for fidelity and consistency in multimodal content creation (Wang et al., 12 Mar 2024, Hu et al., 7 Nov 2024, Soni et al., 14 Oct 2024).

2. Technical Foundations and Core Algorithms

Agent-based frameworks for video understanding and generation implement several technical advances, including:

Markov Decision Process (MDP) Formulation: Frame selection and fast-forwarding modeled as an MDP. Actions correspond to frame skips, states encoded by deep features, and rewards balance coverage versus skip penalties (Lan et al., 2020, Lan et al., 2023).
Consensus and Optimization Algorithms: In DMVF, consensus is achieved via maximal consensus procedures over neighborhood-evaluated importance scores:

$x_i^j[0] = \frac{1}{|V_i|-1} \sum_{k∈V_i, k ≠ j} \exp(-\alpha \|x_j - x_k\|_2)$

and updated via pairwise maximization over rounds equal to network diameter.

Chain-of-Thought Reasoning with Planning and Uncertainty: LLM agent steps include answer prediction, confidence evaluation, and plan adjustment for information retrieval; external tool confidence estimates are integrated to filter noisy context and trigger additional queries (Wang et al., 15 Mar 2024, Zhi et al., 6 Apr 2025).
Memory-Augmented Architectures: Unified memory structures combine event-level segment captions and object-centric tracking, computed via multi-feature ensemble similarity metrics (e.g., weighted sum of CLIP and DINOv2 features for re-ID across frames) (Fan et al., 18 Mar 2024, Fan et al., 31 Dec 2024).
Tree-Search and Coarse-to-Fine Exploration: Curiosity-driven agents (VCA (Yang et al., 12 Dec 2024)) actively explore via tree search across segments guided by intrinsic rewards from VLM-generated chain-of-thought explanations. VideoChat-A1 (Wang et al., 6 Jun 2025) decomposes video shots and recursively partitions/subdivides them based on clustering and semantic deviation metrics for fine-grained reasoning.
Multi-Perspective Reflection: Reward-driven agents use critic-generated feedback for iterative answer improvement, fusing or selecting among conservative, neutral, and aggressive reflections based on sub-question-triggered updates (Zhou et al., 2 Jun 2025).

3. Memory Systems and Zero-Shot Tool Use

Advanced VideoAgents distinguish themselves by sophisticated memory designs and flexible tool integration:

Memory Construct	Description	Purpose
Temporal Memory	Segment-level event captions/features	Stores global timelines
Object Memory	Tracks object states over time; 3D box, category, state	Detailed reasoning, retrieval
Persistent Object Memory	Egocentric + sensor data for 3D object/person tracking	Dynamic scene understanding
History Buffer	Sequential tool-call results	Contextual planning

Agents invoke tools such as segment localization, caption retrieval, visual QA, and SQL-like memory querying through LLM-driven natural language prompts without further fine-tuning, supporting high adaptability and zero-shot transfer (Fan et al., 18 Mar 2024, Fan et al., 31 Dec 2024).

4. Performance Metrics, Experimental Results, and Benchmarks

VideoAgent approaches are empirically validated across a range of benchmarks:

Coverage and Processing Rate: DMVF and MFFNet achieve coverage improvements up to ~25% over baselines (e.g., FFNet, clustering) and processing rates as low as 5% on the VideoWeb and CarlaSim datasets (Lan et al., 2020, Lan et al., 2023).
Benchmark Accuracy: VideoAgent (Wang et al., 15 Mar 2024) reaches 54.1% and 71.3% zero-shot accuracy on EgoSchema and NExT-QA, surpassing LLoVi and other dense sampling agents. VideoAgent2 (Zhi et al., 6 Apr 2025) improves SOTA by 8.6% (subset) and 0.8% (full set). Memory-augmented agents report average increases of 6.6% (NExT-QA) and 26.0% (EgoSchema) over baselines (Fan et al., 18 Mar 2024).
Complex Task Evaluation: Agent-based video trimming (AVT) demonstrates superior mAP and precision on standard and custom benchmarks, with evaluation agent and human studies confirming narrative coherence (Yang et al., 12 Dec 2024). ReAgent-V achieves up to 6.9% and 9.8% improvement in generalization and VLA alignment tasks (Zhou et al., 2 Jun 2025). Chain-of-shot reasoning (VideoChat-A1 (Wang et al., 6 Jun 2025)) yields up to 10.8% higher accuracy over baseline agents with reduced inference time.

5. Representative Applications and System Integration

VideoAgent frameworks find applications across domains:

Surveillance and Situation Awareness: Multi-agent fast-forwarding and summarization of security camera feeds for event detection with reduced resource consumption (Lan et al., 2020, Lan et al., 2023).
Long-form Video Understanding and QA: Efficient, selective reasoning over educational, entertainment, sports, and medical videos, supporting zero-shot interaction, temporal/cause analysis, and segment annotation (Wang et al., 15 Mar 2024, Zhi et al., 6 Apr 2025, Kugo et al., 25 Apr 2025, Yang et al., 12 Dec 2024).
Embodied AI and Robotics: Active scene memory construction from egocentric video plus sensory inputs for manipulation, planning, and dynamic object tracking (Fan et al., 31 Dec 2024, Soni et al., 14 Oct 2024).
Video Generation and Storytelling: Agent-driven multimodal synthesis, evolutionary RAG-based workflows, cross-agent coordination for protagonist and style fidelity in CSVG (Wang et al., 12 Mar 2024, Hu et al., 7 Nov 2024).
Trimming, Summarization, and Highlight Selection: Agent systems for structuring, filtering, and composing user-generated videos into coherent final outputs, benchmarked for both machine and human-perceived quality (Yang et al., 12 Dec 2024).

6. Agent Specialization, Multimodality, and Future Directions

Agent modules are increasingly refined for specialized multimodal tasks—scene graph construction, vision-language interaction, segment partitioning, and real-time reward-driven reflection. Trends include the integration of uncertainty-aware reasoning, active memory systems adapted to scene dynamics, greater autonomy in tool invocation (e.g., OmAgent (Zhang et al., 24 Jun 2024)), support for rich, coherent video content generation, and efficient handling of heterogeneous inputs.

Planned directions involve improvement of consensus protocols in distributed networks, more sophisticated central controller designs, scaling to dynamic wireless sensor environments, extension to audio modalities, and broader real-world deployments. The multilayered, extensible nature of these paradigms positions VideoAgent as a foundational concept for intelligent video understanding and synthesis in edge computing, robotics, and future multimedia content systems.

7. Relation to Other Agentic and Multimodal Systems

VideoAgent systems show continuity with agent-based reinforcement learning, tool-use agents in LangChain and modular LLM architectures, as well as recent reward-driven self-corrective frameworks. Related projects (e.g., VideoChat-A1 (Wang et al., 6 Jun 2025), VCA (Yang et al., 12 Dec 2024), OmAgent (Zhang et al., 24 Jun 2024), ReAgent-V (Zhou et al., 2 Jun 2025), MultiAgents (Kugo et al., 25 Apr 2025), ViQAgent (Montes et al., 21 May 2025)) further expand the agentic role in multi-shot reasoning, curiosity-driven exploration, dynamic reward-assignment, and open-vocabulary grounding validation.

A plausible implication is that agentic frameworks for video understanding will continue to evolve toward more generalist, memory-augmented, and adaptive paradigms that can orchestrate diverse multimodal tools, reasoning strategies, and sensory sources for robust comprehension and efficient processing of large-scale video data.