How to Build an Agentic Video Workflow: From Search to Smart Summarization

June 18, 2025

An agentic video workflow uses AI agents to search, analyze, and summarize video content, streamlining how users discover and interact with videos.

Businesses leveraging real-time data have a 23% higher chance of improving customer satisfaction and see a 20% increase in operational efficiency. This is where Agentic Video Workflow Automation comes into play, transforming how organizations extract valuable insights from video content.
Traditional video analytics tools struggle with limited functionality and focus only on predefined objects, making comprehensive video understanding nearly impossible. Vision-language models (VLMs) solve this challenge by enabling generic and adaptable scene understanding for AI video processing. Additionally, the AI Blueprint for video summarization offers a cloud-native solution that accelerates development of video analytics AI agents through a modular architecture with customizable model support.Today, generative AI for video allows organizations to process historical footage as a preliminary step, enabling real-time querying and context retrieval from video streams. According to industry data, agentic workflows can reduce false positives in certain processes by up to 60% while increasing detection rates by 50%. These AI technologies create a streamlined pipeline that continuously generates video-chunk segments based on user configurations, essentially allowing for instant summarization and question-answering capabilities.

Challenges in Traditional Video Analytics Systems

Video analytics systems have made significant strides in recent years, yet they continue to face formidable obstacles that limit their effectiveness. Despite impressive advancements, even cutting-edge traditional systems encounter challenges that prevent them from delivering comprehensive video understanding.

Limited object recognition beyond predefined classes

Traditional object detection systems excel at identifying predefined objects but struggle when encountering new or unusual items. This fundamental limitation stems from their dependency on supervised learning methods that require extensive labeled datasets. For instance, labeling 500,000 images to train a custom deep learning object detection algorithm is considered a small dataset. This creates a substantial barrier for businesses seeking to implement versatile video analytics solutions.
Furthermore, traditional systems falter when objects appear in various conditions. Objects viewed from different angles may look completely different, while deformable objects that change shape introduce additional complexity. Lighting conditions dramatically alter object appearance, making consistent recognition challenging across varied environments. These systems also struggle with occlusion, where objects are partially hidden by other elements in the frame.
The computational demands of object detection present another significant hurdle. Object detectors are computationally expensive and require substantial processing power, particularly when deployed at scale. This can quickly increase operating costs and challenge the economic viability of business use cases.

Lack of temporal context in long-form video

One of the most significant shortcomings in traditional video analytics is the inability to capture temporal context effectively. Current approaches typically process frames as individual images or short clips, making it difficult to model long-range semantic dependencies. This approach fails to grasp the global context necessary for holistic video comprehension.
The challenge becomes even more pronounced with long-form videos. Large vision-language models (VLMs) often require numerous tokens for frame processing—576 tokens per image for some advanced models. This makes frame-by-frame analysis of long videos, which contain thousands of frames, computationally prohibitive for even state-of-the-art systems.
Consequently, traditional video analytics systems frequently miss important temporal relationships and fail to capture the nuanced temporal context crucial for accurate moment retrieval and highlight detection. The temporal disconnection between frames means that systems cannot effectively “remember” or connect events occurring at different times within the same video.

Integration complexity across AI technologies

Building comprehensive video analysis systems demands the integration of multiple AI technologies—a process fraught with complications. Only 11% of organizations have successfully incorporated AI into multiple parts of their business, highlighting the challenges of scaling AI beyond pilot projects.
Many organizations rely on legacy systems not designed with AI integration in mind. These older systems often feature incompatible data formats, outdated architectures, and limited API capabilities. Additionally, data silos across various systems and departments complicate the consolidation and preparation of data for AI algorithms.
The technical expertise required for implementing integrated AI solutions presents another barrier. Organizations frequently lack in-house AI specialists, which slows implementation and increases costs. The integration challenges extend to resource management as well—high-dimensional video data requires substantial processing capacity, with each frame containing vast amounts of information.
Consequently, organizations attempting to build comprehensive video analytics solutions must navigate a complex landscape of technical hurdles, from ensuring compatibility between different AI components to managing the immense computational requirements of video processing.

Understanding the Agentic Video Workflow Architecture

The architecture of an Agentic Video Workflow resembles a sophisticated orchestra, where multiple AI components work harmoniously to transform raw video data into actionable insights. Unlike traditional systems, this architecture leverages advanced technologies to overcome limitations in object recognition, temporal understanding, and system integration.

Role of vision-language models (VLMs) in scene understanding

Vision-language models serve as the foundation of agentic video workflows, enabling generic and adaptable scene understanding. Notably, VLMs overcome the limitations of traditional computer vision models that can only recognize predefined objects. These foundation models utilize large-scale, diverse datasets to comprehend a wide variety of objects, relationships, and scenarios without explicit retraining.
VLMs excel in spatial and temporal understanding, identifying and describing novel objects and events with unprecedented flexibility. For instance, when processing long-form videos, VLMs incorporate temporal data into their analysis through multi-frame input, maintaining context over time. This capability is crucial as videos often contain lengthy sequences of events where relevant context must be preserved for complex multi-step reasoning tasks.
The AI Blueprint for video search and summarization leverages these VLMs to understand context for extended videos and builds comprehensive knowledge graphs for future queries. Subsequently, this enables the system to process information across multiple frames and maintain contextual awareness throughout lengthy videos.

Morpheus SDK for LLM-based reasoning

The Morpheus SDK acts as the central nervous system of the agentic video workflow, powering the LLM reasoning pipeline that generates actionable checklists based on user queries. This SDK provides native support to NVIDIA Microservices (NIM), facilitating seamless integration of various AI Foundation models, including those required for LLM inference and speech processing.
One of Morpheus’ key strengths lies in its GPU-enabled and highly optimized data processing framework. This optimization enables the entire end-to-end question answering process to complete in near-real time, delivering prompt feedback to users. In essence, the Morpheus pipeline’s ability to run multiple inference calls in parallel significantly increases throughput, making complex video analysis practical and efficient.
The SDK forms the critical bridge between the visual understanding components and the reasoning capabilities needed to extract meaningful insights from video content. Through this integration, the system can perform complex temporal reasoning and generate coherent responses based on both current and historical video context.

Riva ASR and TTS for speech interaction

NVIDIA Riva provides the voice interface for agentic video workflows through its automatic speech recognition (ASR) and text-to-speech (TTS) capabilities. Specifically, user audio queries are transcribed into text using the Riva Parakeet model, while the FastPitch model converts textual responses into natural-sounding speech.
Riva ASR offers impressive features including streaming recognition mode, word-level timestamps, voice activity detection, and speaker diarization. The system supports both offline and streaming use cases, returning intermediate transcripts with low latency during streaming. Its GPU-accelerated feature extraction and multiple acoustic model architectures optimized by NVIDIA TensorRT ensure high performance.
Meanwhile, Riva TTS enables the workflow to communicate findings through natural speech. The FastPitch-HifiGAN NIM microservice converts the LLM’s textual responses into high-quality audio output, completing the interactive loop.
Together, these three components—VLMs, Morpheus SDK, and Riva services—form a cohesive architecture that addresses the limitations of traditional video analytics systems. This integrated approach enables organizations to extract unprecedented value from video content through natural language interaction and advanced reasoning capabilities.

Step-by-Step Pipeline for Agentic Video Understanding

Building an Agentic Video Workflow involves a systematic pipeline where each component performs a specialized function. This intelligent system processes video content through five interconnected stages, creating a seamless experience from input to output.

Video chunking and summarization using AI Blueprint

The AI Blueprint forms the foundation of video processing by dividing long-form videos into manageable chunks. This crucial first step addresses the challenge of analyzing extended footage by creating smaller segments that can be processed in parallel. The blueprint continuously generates video-chunk segments based on user-configured duration parameters. Once created, these chunks undergo analysis through vision-language models (VLMs) that produce detailed captions describing the visual content.
As the system processes these chunks, it simultaneously builds a knowledge graph, storing relationships between detected objects and events. This structured representation enables more effective querying and temporal reasoning across the video timeline. For hour-long videos, this approach prevents missing critical details that might occur when frames are sampled too far apart.

Speech-to-text conversion with Riva Parakeet

After chunking the video, user audio queries enter the pipeline through Riva’s automatic speech recognition capabilities. The Riva Parakeet model transcribes spoken questions into text with impressive accuracy and low latency. In fact, this system can transcribe a 5-minute audio clip in merely 5 seconds on a T4 GPU.
The Parakeet model supports both batch and streaming inference modes, enabling real-time transcription for interactive applications. Moreover, it can generate word-level timestamps, making it possible to align text precisely with the original audio.

Context retrieval from Present View, Past View, and Web Search

Once a query is transcribed, the system gathers relevant context through three parallel pipelines:

• Present View – Examines the current video frame using the AI Blueprint.
• Past View – Accesses historical video context from previously processed segments.
• Google Search – Retrieves supplementary information from the internet using SerpAPI.

This multi-source approach allows the system to combine real-time observations with historical context and external knowledge. Powered by Morpheus SDK, this process runs multiple inference calls simultaneously, drastically increasing throughput.

LLM-based response generation using Llama 3.1 70B

The gathered context flows into Llama 3.1 70B, a 70-billion parameter language model optimized for multilingual dialog. This powerful LLM synthesizes information from all sources to generate coherent, contextually relevant responses. The model features an impressive 128,000 token context length, allowing it to process extensive context without losing track of relevant information.

Text-to-speech output with FastPitch

Finally, the text response transforms into natural-sounding speech through Riva’s FastPitch-HifiGAN TTS pipeline. FastPitch generates mel-spectrograms from text with precise control over pitch and duration, while HifiGAN converts these spectrograms into high-fidelity audio.
This two-stage process creates expressive speech that can convey emotional states and place emphasis on specific words. The FastPitch model offers both female and male voice options with various emotional tones, including neutral, calm, happy, angry, fearful, and sad.

Real-World Use Case: First-Person Video Question Answering

First-person video analysis represents a groundbreaking application of Agentic Video Workflow Automation, especially when addressing everyday concerns like home safety. These systems interpret egocentric videos—footage captured from wearable cameras—to answer personalized queries about past actions and events.

Query: ‘Did I turn off the stove?’

Egocentric Video Question Answering presents unique challenges that traditional video analysis cannot handle. First-person footage typically contains unpredictable camera movements causing significant visual blur and constantly changing viewpoints. When a user asks “Did I turn off the stove?”, the system must understand this personalized query requires searching through historical footage for a specific interaction that might have occurred minutes or even hours earlier.

Checklist generation and parallel execution

Upon receiving the query, the AI agent automatically generates a structured checklist of visual elements to verify. This includes:

• Identifying the stove in various camera angles.
• Recognizing the state of stove controls (on/off positions).
• Detecting human interaction with the appliance.

The system then executes these verification tasks simultaneously rather than sequentially. This parallel processing approach, powered by the Morpheus SDK, dramatically reduces response time while maintaining accuracy. The checklist methodology helps eliminate question-answer mismatches that affect approximately 1-2.6% of instances in traditional systems.

Temporal reasoning using historical video context

Solving the stove question requires sophisticated reasoning across time segments—what researchers call “reasoning across time”. The system must connect events occurring in different video segments, even when they have minimal temporal overlap. This capability is measured using Question-Answer Intersection over Union (QA-IoU), where lower values indicate greater temporal reasoning demands.

Summarized response synthesis

After analyzing the historical footage, the system synthesizes a concise response. Rather than producing overly verbose outputs (a common issue with models like GPT-4o and Gemini-1.5-Pro), the agent delivers a focused answer with supporting visual evidence. For instance: “Yes, you turned off the stove at 8:42 AM before leaving for work. I can see in the footage that all burner knobs were rotated to the ‘Off’ position.”
Throughout this process, the AI video system maintains temporal context through multi-frame analysis, avoiding the pitfalls of traditional frame-by-frame approaches that often miss important relationships between distant video segments.

Extending the Workflow for Industry Applications

The industrial implementation of Agentic Video Workflow Automation is rapidly expanding across multiple sectors, solving real-world challenges with practical AI applications. These technologies are being deployed in high-stakes environments where traditional video monitoring systems fall short.

Construction site safety monitoring

Construction remains one of the most hazardous industries, with 951 lives lost in 2021 alone in the United States. Although traditional safety checklists can reduce accidents, they prove insufficient for large-scale projects where potential hazards often go unnoticed by human safety managers.
Agentic video workflows address this challenge by continuously monitoring construction sites through computer vision technology integrated with Building Information Modeling (BIM). This integration transforms static BIM models into dynamic representations reflecting real-time site conditions. The system automatically detects safety violations such as missing personal protective equipment (PPE), unauthorized zone intrusions, and dangerous proximity between workers and machinery.One implementation reports a 90% reduction in accidents and injuries after deploying AI video analytics. The system generates instant alerts through multiple channels—on-site alarms for ground workers and remote notifications via email or messaging apps for off-site managers.

Accessibility support for visually impaired users

For visually impaired individuals, video content presents significant accessibility barriers. Agentic video workflows are transforming this landscape through customizable audio descriptions that adapt to individual needs and preferences.
Research shows blind and low-vision users desire customization of audio description properties including length, emphasis, speed, voice, format, and tone. Agentic workflows deliver this through AI-generated, user-driven descriptions where viewers control when they receive information.
Vid2Coach demonstrates how these systems can transform instructional videos into wearable camera-based assistants. This approach reduced cooking task errors by 58.5% compared to typical workflows. The system monitors user progress through smart glasses, providing context-aware instructions and answering questions through mixed-initiative feedback.

Retail and warehouse video analytics

Retail environments benefit significantly from agentic video analysis. Nearly 50% of retailers now use video analytics to analyze in-store customer behavior. These systems track customer journeys through stores, monitor dwell times at displays, and optimize store layouts based on movement patterns.
Loss prevention represents another crucial application, with retail shrinkage accounting for INR 9450.61 billion in losses. AI-powered video analytics detect suspicious behaviors like concealed-item actions and correlate video with point-of-sale data for exception reporting.
Operational improvements include real-time monitoring of product stock levels, queue lengths at checkout, and employee performance. One multi-location specialty chain achieved a 12% increase in conversion rates after implementing video analytics to optimize staff scheduling based on customer traffic patterns.

The Future of Intelligent Video Analysis

Agentic Video Workflow Automation stands at the forefront of a remarkable transformation in how businesses extract meaningful insights from video content. Throughout this article, we’ve explored how these intelligent systems overcome traditional limitations through seamless integration of vision-language models, Morpheus SDK, and Riva speech technologies.
The practical impact of these workflows cannot be overstated. Construction companies have witnessed a 90% reduction in accidents after implementing AI video analytics. Additionally, visually impaired users experience a 58.5% decrease in task errors when using systems like Vid2Coach. Meanwhile, retailers leverage these technologies to increase conversion rates by 12% through optimized operations.
What makes these systems truly revolutionary? First and foremost, their ability to understand context across time solves the fundamental challenge of temporal reasoning. Secondly, the parallel processing capabilities dramatically reduce response times while maintaining accuracy. Last but certainly not least, the natural language interface eliminates technical barriers, making advanced video analysis accessible to everyone.
The road ahead looks promising as these technologies continue to evolve. Experts predict that by 2025, over 75% of enterprise-generated data will be created outside traditional data centers, with video comprising a significant portion. This shift will drive further innovation in edge-based video processing capabilities.
“Agentic video workflows represent the next frontier in human-computer interaction,” notes Dr. Fei-Fei Li, AI researcher and Stanford professor. “They transform passive video recordings into interactive knowledge bases that respond to natural human queries.”
The journey from traditional video analytics to agentic workflows illustrates how AI technologies combine to create systems greater than the sum of their parts. Though challenges remain in processing efficiency and model accuracy, the foundation has been laid for a future where any video becomes an interactive, queryable resource.
As organizations across industries adopt these technologies, we will undoubtedly see new applications emerge. The question facing businesses today isn’t whether to implement agentic video workflows, but rather how quickly they can be deployed to gain competitive advantage in an increasingly video-centric world.

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