Agentic Video Intelligence (AVI)

Updated 22 November 2025

Agentic Video Intelligence is a paradigm that uses LLM-driven agents to decompose complex video analysis tasks and orchestrate modular tools.
It employs iterative, feedback-driven reasoning to dynamically refine video understanding through adaptive retrieval and structured knowledge.
AVI systems demonstrate significant gains in scalability, interpretability, and efficiency across tasks like video QA, entity extraction, and segmentation.

Agentic Video Intelligence (AVI) is a paradigm in video understanding that employs autonomous, LLM-driven agents to iteratively decompose complex video analysis tasks, orchestrate specialized tool use, and dynamically adapt strategies based on intermediate observations and reasoning. Unlike monolithic vision-language systems, AVI systems partition reasoning from perception and retrieval, employ modular toolkits, maintain structured video knowledge representations, and execute workflows that mirror human-like hypothesis testing, evidence gathering, and iterative refinement. AVI enables scalable, interpretable, and efficient solutions to long-form video question answering, entity extraction, segmentation, analytics, and generation, as demonstrated by recent advances across multiple domains.

1. Definition and Core Principles

Agentic Video Intelligence denotes video analytics and understanding systems that transcend static, single-pass, or rigid pipelines by introducing agentic behaviors: reasoning-driven task decomposition, dynamic invocation and orchestration of modular tools, adaptive retrieval, feedback-driven refinement, and goal-directed interaction with structured knowledge. AVI agents operate with autonomy, making context-sensitive decisions about which video segments, modalities, or analytical paths to pursue, and revising strategies in light of intermediate evidence or failures. This architecture includes:

Task decomposition: LLM-based agents break down complex natural language questions into subtasks, each potentially requiring multimodal retrieval, analysis, or generation (Yuan et al., 12 Jun 2025).
Tool orchestration: Agents select from a toolkit that may include retrievers, perceivers, video browsers, subtitle extractors, entity detectors, and more (Yuan et al., 12 Jun 2025, Gao et al., 18 Nov 2025).
Iterative and feedback-driven reasoning: AVI agents engage in multi-phase or looped reasoning—retrieving candidate evidence, conducting local analysis, reviewing and critiquing outputs, and restarting cycles to clarify uncertainties (Patel et al., 19 Nov 2025, Gao et al., 18 Nov 2025).
Structured knowledge integration: Video content is indexed and abstracted via knowledge graphs, entity graphs, or embedding databases, enabling efficient, semantically aware retrieval (Yan et al., 1 May 2025, Gao et al., 18 Nov 2025).
Modularity and extensibility: AVI frameworks are not tied to fixed models; instead, their agents are designed to call, combine, or replace any compatible models or tools based on their reasoning (Yuan et al., 12 Jun 2025, Rosa, 3 Mar 2025).

2. Architectures and Algorithms

AVI systems exhibit heterogeneity in architectural instantiation but share key agentic scaffolding. Notable patterns include:

Plan–Act Dual Agent Architectures: UniVA features a Planner Agent that interprets goals, decomposes tasks, and admits mid-course replanning, and Executor Agents that realize stepwise tool calls, all backed by a three-tiered memory system (global, task, user memory) for persistent context (Liang et al., 11 Nov 2025).
Three-Phase Reasoning Pipelines: Systems such as AVI (Qwen3-32B ensemble) decompose reasoning into Retrieve (global candidate generation), Perceive (local grounding, attribute detection), and Review (reflection, self-critique, possible return to perception) phases (Gao et al., 18 Nov 2025). Phases are implemented as MDP transitions, with agent state comprising history, tool observations, and current phase.
Agentic Search and Tool-Driven Loops: Deep Video Discovery (DVD) and Agentic Keyframe Search (AKeyS) agents leverage LLMs to guide dynamic search or tree expansion algorithms over segment-indexed videos, employing heuristic and cost functions formalized as $h(n)$ and $g(n)$ analogous to A*-style planning (Fan et al., 20 Mar 2025, Zhang et al., 23 May 2025).
Component and Workflow Examples:

| System | Reasoning Core | Toolset & Knowledge | Iterative Control | |--------------------|---------------------|---------------------|----------------------| | VideoDeepResearch | Text-only LRM (e.g., DeepSeek-R1) | Video/Subtitle/Visual retrievers; perceivers | Plan & Invoke / Synthesize & Answer (Yuan et al., 12 Jun 2025) | | RAVEN | VLM + LLM orchestrator | Schema-induced entity extraction | Pipeline: Categorize → Schema Gen → Extraction (Rosa, 3 Mar 2025) | | AVATAAR | Modular agent + Rethink Module | Global summary, temporal aligner | Think–Retrieve–Rethink loop (Patel et al., 19 Nov 2025) | | CAViAR | LLM agent + Critic | ASR/segment retrieval, QA modules | Critic-augmented selection (Menon et al., 9 Sep 2025) |

Many AVI agents utilize explicit chain-of-thought (CoT) propagation, beam search, or self-evaluation confidence thresholds to determine search or reasoning termination (e.g., dual sub-routine confidence scores $c_1$ , $c_2$ in AKeyS (Fan et al., 20 Mar 2025)). Others employ multi-agent or model-ensemble routing, dynamically selecting the best-suited module set for each input (Xing et al., 9 Oct 2025, Liang et al., 11 Nov 2025).

3. Structured Knowledge and Tool Interoperation

Structured knowledge representation is central to AVI. Approaches include:

Knowledge Graphs / Entity Graphs: AVAS uses an Event Knowledge Graph (EKG) with nodes for temporally ordered events and entities, and relation sets capturing temporal, semantic, and participation links, continuously updated at >5 FPS for real-time deployments (Yan et al., 1 May 2025).
Multi-Granularity Video Databases: DVD builds hierarchical video indexes including subject registries, segment-level captions, embeddings, and full-resolution frames, enabling efficient nearest-neighbor and fine-grained frame-level queries (Zhang et al., 23 May 2025).
Schema-Driven Entity Extraction: RAVEN induces domain-specific extraction schemas via LLMs for each video category and prompts VLMs to parse structured entities with attribute filling, attaining substantially higher recall than unimodal baselines (Rosa, 3 Mar 2025).

Tool selection is managed by LLM-driven policies that parse task context, select tool modules by semantic compatibility or anticipated evidence yield, and synthesize their outputs using context-sensitive aggregation strategies. These policies can be formalized as optimization over cost–accuracy trade-offs: $\min_\pi \{L_{\text{acc}}(\pi) + \lambda \cdot \mathbb{E}[\text{Cost}(\pi)]\}$ where $\pi$ denotes the policy over tool calls (Yuan et al., 12 Jun 2025).

4. Iterative Reasoning, Self-Critique, and Adaptivity

Iterative, feedback-intensive workflows distinguish AVI from static systems. AVATAAR explicitly implements a Think–Retrieve–Rethink loop, where a global summary is leveraged to anchor context, queries are adaptively refined, local evidence is repeatedly aligned, and a Rethink Module triggers repair or elaboration sub-cycles until sufficient confidence or budget exhaustion (Patel et al., 19 Nov 2025). Similarly, systems such as AKeyS employ LLM self-evaluation and temporal summarization sub-routines to assess answer confidence and search sufficiency (Fan et al., 20 Mar 2025). The CAViAR agent utilizes a separate LLM critic to rank reasoning trajectories, selecting the most probable correct chain-of-thought among candidate reasoning sequences (Menon et al., 9 Sep 2025).

UniVA generalizes these ideas: multi-agent Plan–Act workflows admit error recovery via diagnostic step reporting and replanning, supporting compositional video manipulation or editing through chained, rollback-capable task execution (Liang et al., 11 Nov 2025). Such mechanisms confer improved generalization, resilience to module or retrieval errors, and enable inspection of reasoning paths for interpretability.

5. Applications, Benchmarks, and Quantitative Performance

AVI methodologies have been deployed for diverse tasks, including long-form video QA, temporal and spatial localization, open-ended analytics, entity extraction, video generation/abstraction, segmentation, and video quality assessment.

Empirically, AVI systems repeatedly set state-of-the-art results:

Video QA: VideoDeepResearch outperforms baseline MLLMs and RAG variants on MLVU, LVBench, and LongVideoBench, achieving +9.6%, +6.6%, +3.9% improvements, while using only 32 frames per inference (Yuan et al., 12 Jun 2025).
Long-Form Analytics: AVAS achieves 62.3% (LVBench), 64.1% (VideoMME-Long), and 75.8% (AVAS-100), consistently surpassing retrieval-augmented or context-window-limited systems (Yan et al., 1 May 2025).
Entity Extraction: RAVEN attains 85% recall for person entities, outperforming NER/OCR/captioning baselines (<60%) (Rosa, 3 Mar 2025).
Temporal and Technical Reasoning: AVATAAR delivers +8.2% gain in narrative comprehension and +5.6% gain in temporal reasoning over RAG-only baselines (Patel et al., 19 Nov 2025).
Video Generation: Preacher establishes structured agentic video abstract generation, surpassing Sora, Kling 1.6, and OpenAI-o3-mini pipelines on all axes (accuracy, professionalism, alignment) (Liu et al., 13 Aug 2025).
Segmentation: M²-Agent outperforms prior supervised and training-free segmentation pipelines on RVOS MeViS (mIoU 46.1) and Ref-AVS (36.26) (Tran et al., 14 Aug 2025).
Quality Assessment: Q-Router matches or exceeds single-expert and end-to-end VQA systems and delivers interpretable artifact heatmaps (Xing et al., 9 Oct 2025).

6. Interpretability, Generalization, and Limitations

Strengths of AVI frameworks include:

Interpretability: Explicit reasoning logs, tool-invocation breakdowns, workflow traces, and artifact localizations offer unprecedented transparency into decision processes (Xing et al., 9 Oct 2025, Gao et al., 18 Nov 2025).
Modularity: Systems can plug-and-play improved retrievers, perception modules, or policy learners without end-to-end retraining (Yuan et al., 12 Jun 2025, Gao et al., 18 Nov 2025).
Long-Horizon Adaptivity: Agents maintain memory, summary states, or graph context, preserving coherence and continuity across multi-step or interactive workflows (Liang et al., 11 Nov 2025).
Resource efficiency: Dynamic evidence search reduces visual token usage by up to 25% of competing systems at equivalent accuracy (Fan et al., 20 Mar 2025, Zhang et al., 23 May 2025).

However, several constraints remain:

LLM and Tool Latency: Feedback loops and repeated tool calls increase inference time relative to monolithic models (Fan et al., 20 Mar 2025, Patel et al., 19 Nov 2025).
Dependence on Upstream Fidelity: Poor captions, misaligned schemas, or module errors can propagate and degrade agentic performance (Gao et al., 18 Nov 2025, Rosa, 3 Mar 2025).
Lack of On-Policy Learning: Most systems are training-free or rely on zero/few-shot prompting; reinforcement learning of tool-invocation and workflow policies is an open direction (Yuan et al., 12 Jun 2025).
Limited Dynamic Knowledge Update: Static databases or schema sets cannot be refined at inference without pipeline augmentation (Gao et al., 18 Nov 2025).

7. Prospective Developments and Synthesis

Future AVI research trajectories include:

End-to-End Reinforcement Learning: Learning policy networks for tool selection, workflow adaptation, and dynamic retrieval under latency/accuracy constraints (Yuan et al., 12 Jun 2025).
Memory-Augmented Agents: Implementation of persistent episodic or semantic memory to improve reasoning with long-term context (Liang et al., 11 Nov 2025).
Parallel Tool Execution and Gating: Reducing inference bottlenecks via concurrent tool use and lightweight meta-policies (Gao et al., 18 Nov 2025).
Dynamic Schema and Graph Update: Live database enrichment and continual schema evolution driven by user or agent feedback (Gao et al., 18 Nov 2025, Rosa, 3 Mar 2025).
Broader Modalities: Incorporating audio, scene-graph, and real-time sensor data in agentic reasoning cycles (Yuan et al., 12 Jun 2025, Lin et al., 13 Apr 2025).