TLDR: Context engineering—not just bigger models—is the key to reliable video understanding applications.

• The Context Problem: Most LLM failures stem from insufficient, outdated, or poorly formatted context—not weak models.

• Four Pillars of Video Context Engineering:

  • Write Context: Convert video into descriptive, machine-ingestible text, structured data, or vector embeddings.

  • Select Context: Choose only the most relevant pieces of context for the specific task through semantic search and filtering.

  • Compress Context: Condense information through summarization and abstraction without losing critical meaning.

  • Isolate Context: Structure and segregate context to prevent model confusion between different information sources.

• Advanced Strategies:

  • Memory architectures that combine short-term "working" memory with long-term knowledge bases

  • Dynamic retrieval through tools that actively seek additional context when needed

  • Structured context packaging in clear, unambiguous formats (like JSON)

• Real-World Applications: These techniques power sports highlight automation, security video analysis, and content-aware advertising—reducing manual work while increasing accuracy.

• Future Direction: As models become commoditized, the competitive edge will come from how effectively context is engineered—not just raw model performance.

Introduction

Consider this: ask an LLM about your company’s return policy, and it may confidently invent rules that don’t exist. Or ask a RAG system for last quarter’s revenue, and it may serve up an irrelevant document about 2019 projections. These aren’t failures of model reasoning—most LLMs can handle logic and numbers just fine—but failures of context.

The same LLM goes from fabricating to flawlessly accurate when given the right context. Feed it your actual return policy, a customer’s order history, and current inventory levels—and suddenly it delivers precise, personalized support. This is context engineering: systematically designing what information goes into the LLM and how it's structured, rather than relying on clever prompts to compensate for missing or messy data.

Most production LLM failures aren’t due to weak models—they stem from insufficient, outdated, or poorly formatted context. Yet teams often obsess over prompt tweaks while their context pipeline is an afterthought. By treating context as a first-class engineering challenge—building systems for dynamic retrieval, structured extraction, and intelligent filtering—we turn unreliable demos into products users actually trust.

At Twelve Labs, we apply this principle to video with unique insight. Video isn’t just about objects and words—it’s about meaning through sequence. Filmmakers call this the Kuleshov effect: viewers derive emotional interpretation not from a single shot, but from how shots are juxtaposed—placing the same neutral face beside different images (a bowl of soup, a coffin, a woman) reshapes its perceived emotion entirely

Our platform doesn’t just scale up model size; it engineers video context—including temporal order as meaning. By curating and structuring what the model “sees” and in what sequence, we mitigate hallucination and misinterpretation. The result? More accurate, grounded outputs—and a system users can trust, because the answers reflect the real, temporally-aware narrative in the video.

In the rest of this post, we’ll break down how TwelveLabs applies context engineering to video, through Four Pillars of Video Context Engineering, advanced memory and retrieval strategies, and applications that can be unlocked. The goal: to show why context – not just bigger models – will define the next generation of video intelligence.

1 - The Four Pillars of Video Context Engineering

Context is what grounds the raw information present in a video and enables meaningful interpretation—no understanding happens in a vacuum. A static sequence of frames or transcript alone doesn’t convey narrative, intent, or causality without the right framing.

That’s why, at Twelve Labs, our video AI doesn’t just process pixels—it engineers context. We do this through four foundational pillars (as described in depth by the LangChain team): Write Context, Select Context, Compress Context, and Isolate Context. These pillars represent the systematic methods by which we structure, filter, condense, and compartmentalize video data so that our models can reason effectively. Below, we’ll explore each pillar with concrete examples of how they’re implemented in video pipelines.

Adapted From: https://blog.langchain.com/context-engineering-for-agents/

1.1 - Write Context

The first pillar is Write Context – converting video into descriptive, machine-ingestible information. This often means literally writing out context from the video’s raw modalities (images, audio) into text, structured data, or vector embeddings. By generating this textual context, we give the model something to work with beyond pixels.

In practice, “writing context” for video involves tasks like transcription, captioning, and summarization. Imagine a 10-minute safety training video: a context-engineered pipeline might first transcribe the spoken dialogue and describe key visual events. TwelveLabs’ model Pegasus (a video-native language model) can be used to generate a summary or commentary for each scene. Essentially, Pegasus writes out what’s happening in natural language – who’s doing what, when, and where – creating a semantic narrative of the video. This written context becomes the basis for downstream QA or search tasks. It’s much richer than simplistic tags, and it’s tailored to the video content itself.

Crucially, writing context isn’t limited to plain text. We often employ structured outputs. For instance, rather than a raw transcript, the system might produce a JSON document with fields like: {"scene": 5, "timestamp": "02:15", "description": "A person in a red jacket runs across the street as a car approaches."}. This is far more informative for an AI agent. Structured context packaging like this provides clear, digested knowledge to the model without extraneous noise. As the LlamaIndex team emphasizes, structured data formats (like JSON or XML) help logically separate context elements – instructions, video facts, metadata – so the model can parse them without confusion. In our example, a JSON timeline of the video could let the AI quickly pinpoint scene 5 when asked “What happened when the person in the red jacket appeared?”

By writing out context in well-organized text, we set the stage for everything that follows. It establishes the ground truth the AI will reason over. Our customers who use our models leverage this pillar heavily:

• For instance, Marengo (our multimodal embedding model) transforms raw video clips into multimodal embeddings – a numerical form of “written” context that captures semantic meaning. Those embeddings enable powerful search later on.

• Meanwhile, Pegasus can generate textual summaries of clips instantaneously, essentially writing context on demand.

• Together, they ensure that no important detail in the video stays locked in the raw footage – it’s all extracted into words or vectors that their video AI products can use.

1.2 - Select Context

Even after we’ve “written” down video information, we usually end up with far more context than a model can handle in one go. Imagine transcribing an hour-long video – the transcript could be tens of thousands of words. Feeding all of that to an LLM would be inefficient (or impossible, given context window limits). This is where Select Context comes in: choosing the most relevant pieces of context for the task at hand.

Selecting context is essentially an intelligent filtering or retrieval step. Given a user query or a specific AI task, the system must pull out the slices of video data that matter, and ignore the rest. For example, if an analyst asks, “When does the suspect enter the room and what do they say?”, the system should select the relevant scene (where the suspect enters) and the associated transcript lines, rather than dumping the entire video’s transcript. In other words, we treat our written context (from Pillar 1) as a knowledge base and query it semantically.

TwelveLabs’ model Marengo is purpose-built for this pillar. Marengo creates embeddings for video, audio, and text, placing them in a shared vector space. This allows semantic search over video content. Using Marengo, our system can take a natural language query and retrieve the most similar video segments or descriptions. If you ask “The goal where the player celebrates with a backflip,” our search API can surface the clip of the soccer player doing that backflip celebration, even if no explicit tag existed. We’ve essentially given the AI the eyes to find the needle in the haystack.

Context selection extends beyond basic search to include dynamic filtering in agentic workflows. Our upcoming agent Jockey can autonomously gather context through API calls - for instance, when creating sports highlights, it filters game events based on excitement scores or featured players. This approach significantly reduces noise for the model, addressing what the LangChain team notes: LLMs can only reason with what you provide them. By selecting only the most relevant video segments, we prevent hallucinations and improve accuracy. This follows a core RAG principle: better selection yields better results. For practical implementation, see our Weaviate tutorial on video RAG.

1.3 - Compress Context

Even after selecting the most relevant bits of a video, we may still have more data than we’d like – or data that’s too verbose. Compress Context is the strategy of condensing information so that it fits within the model’s input limits and is easier to digest, without losing critical meaning. Compression can happen through summarization, abstraction, or encoding.

Consider a police bodycam video scenario: Let’s say we have 5 minutes of footage around an incident. We can compress the context by highlighting the key facts. TwelveLabs’ Pegasus model often plays this role – it can take a long video segment and generate a shorter synopsis capturing the main points. For example, a 3-sentence summary of that 5-minute footage can be: “Officer approaches a parked car at night; the suspect in a red jacket appears nervous and reaches under seat; officer steps back and radios for backup.” This summary is a fraction of the length but retains the critical details needed for reasoning.

There are multiple ways to compress context in video systems:

• Summarization: as described, using video language models to summarize or describe the video input.

• Temporal compression: dropping redundant frames or merging consecutive moments into a higher-level event. (E.g., compressing 10 frames of a continuous action into one “the action continues” description.)

• Modality filtering: focusing on one modality when others add little. (E.g., if audio carries most information in a lecture video, we might not describe every visual detail, effectively compressing context by ignoring a less useful modality.)

Context compression mirrors what human video editors do when creating highlight reels: condensing essential moments while discarding the rest. Our work with MLSE demonstrated this principle by automatically distilling game footage into key events, achieving 98% efficiency in highlight creation and reducing editing time from 16 hours to just 9 minutes. From a technical perspective, compression techniques like iterative summarization (summarizing chapters, then summarizing those summaries) help overcome token limitations in models. As LlamaIndex notes, summarizing search results before adding them to prompts helps stay within context limits. In our customers’ pipelines, Pegasus can summarize intermediate findings to maximize the signal-to-token ratio, ensuring our models receive only the most relevant information.

1.4 - Isolate Context

The fourth pillar, Isolate Context, is about structuring and segregating the context so that the model isn’t confused by it. Complex video tasks often involve multiple sources of information and multiple steps of reasoning. If we dump everything into one giant blob, the model might get overwhelmed or mix up unrelated information. Isolating context means compartmentalizing different types of context and different stages of the process.

There are a few dimensions to isolating context:

• Isolation by source or type: We keep various context types separate. For example, system prompts (the “rules” for the AI) are isolated from video content data. Similarly, visual descriptions might be kept separate from dialogue transcripts. This could be done with structured prompts (like JSON sections or special tokens) to clearly delineate, say, "scene_description": ... from "speech_transcript": .... Such separation prevents the model from, for instance, interpreting a description as something a user said. It adds clarity.

• Temporal isolation: We ensure that context from a previous segment of the video doesn’t bleed confusion into the next. Instead of carrying over all details from earlier scenes, we might summarize or reset context when moving to a new scene (“episodic memory”). Essentially, treat each scene or chapter in isolation, aside from a distilled summary that connects them. This approach keeps the working context relevant to the current moment in the video.

• Step isolation in agents: In an agent like Jockey, which performs multi-step tool calls and reasoning, we isolate each step’s context. For example, Jockey uses a planner-worker-reflector architecture. The Planner might only see the high-level goal and a summary of progress (isolating it from raw video details), while the Worker sees the specific video segment it needs to analyze (isolating it from other segments). After each step, the Reflector may update an overall state. By isolating what each component sees, we avoid a scenario where, say, the planning logic is distracted by low-level frame data or the analysis step gets confused by the entire plan. Each part gets just the context it needs.

Adapted from: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus

Isolation enhances both clarity and performance optimization. By separating static context (instructions, tool definitions) from dynamic context (observations, queries), we maintain cache efficiency and reduce costs—cached tokens can be ~10× cheaper than uncached ones (a finding from Manus). This approach prevents cross-talk between unrelated information, creating more deterministic, debuggable behavior. When errors occur, we can quickly identify whether the issue stemmed from instructions, video data, or tool results, since each component is compartmentalized. At its core, context isolation follows a "divide and conquer" philosophy: tackle each aspect of the problem with its own clean, focused context.

2 - Advanced Strategies for Video Context Engineering

The four pillars give us a foundation, but building a truly robust video AI system often requires advanced techniques on top. In this section, we explore some cutting-edge strategies that TwelveLabs is employing to push video understanding to the next level: short-term vs. long-term memory architectures, dynamic retrieval with tool orchestration, and structured context packaging. These approaches ensure that our foundation models not only handle one-off queries, but can sustain understanding over time, adapt on the fly, and interface seamlessly with external systems.

2.1 - Memory Architectures: Short-Term and Long-Term

Source: https://langchain-ai.github.io/langgraph/concepts/memory/

Just like humans, AI systems benefit from having both a short-term “working” memory and a long-term knowledge base. For video agents, this is especially important. A video might be hours long (requiring memory of earlier scenes), and an AI could also accumulate knowledge across multiple videos or sessions. We divide memory into two flavors:

• Short-Term Memory: This is the transient memory of the current session or video. In a chatbot context, short-term memory is the recent conversation history; in video, it could be what has happened in the current scene or the running summary of the video so far. Short-term memory is frequently updated and typically fits in the model’s context window directly. One common technique is the sliding window summary: as our models process a video clip by clip or scene by scene, it maintains a rolling summary of the last few minutes so it doesn’t lose context of what just happened. Another example is remembering the user’s last question and the AI’s last answer when the user asks a follow-up about the same video.

• Long-Term Memory: This refers to persistent knowledge stored outside the immediate context window, retrievable on demand. In video understanding, long-term memory might include an index of facts about characters or places from earlier in a movie, stored in a vector database, or metadata from previous videos the agent has processed. It could also mean cumulative learning – e.g. an agent that monitors security cameras might build up a profile of typical activity at a location over weeks. Long-term memory is often implemented via databases or embeddings: for instance, TwelveLabs could embed every scene of a TV series and store those embeddings. When analyzing a new episode, if the agent needs backstory on a returning character, it can query that vector store to retrieve the relevant context from past episodes.

In practice, Marengo + Pegasus enable a memory hierarchy for our video agents. Marengo’s vector embeddings act as the long-term memory – you can embed all historical video data so it’s searchable later. Pegasus, with its ability to summarize and converse about video, handles short-term memory – for example, keeping track of what’s currently happening in the video through incremental summaries or notes. Our agent Jockey is designed to juggle both: Jockey can retrieve from a long-term vector memory (e.g., “find all past surveillance clips where this person appeared”) and also maintain a local state of the immediate task (“what’s been found so far in this clip”).

A novel idea that we are thinking about is to build a memory stack to main multiple layers of context (inspiration from Factory’s Context Stack). The immediate layer contains current scene details or recent interactions, while middle and deep layers store progressively more historical information - from scene summaries to a searchable database of past videos. Rather than overwhelming the model with all memories at once, we want to employ strategic retrieval rules: always include immediate context, selectively include summaries, and perform targeted retrieval from long-term memory only when necessary. This approach optimizes token usage through dynamic summarization, compressing older information while preserving essential meaning - similar to human memory's natural consolidation process.

In essence, short-term memory gives our models coherence in understanding a single video or conversation, while long-term memory gives them continuity across time and data. Balancing the two is key. The emerging best practices (reflected in frameworks like LlamaIndex memory modules) involve using vector stores for long-term info and on-the-fly summarization for short-term history. Twelve Labs’ products incorporate these ideas so that whether your video AI is answering a question about a video scene or generating a storyboard from multiple videos, it doesn’t lose track of context even as time goes on.

2.2 - Dynamic Retrieval and Tool Orchestration

Advanced video agents do more than passively analyze what’s in front of them – they can actively seek out more context as needed. This is the idea of dynamic retrieval: on the fly, the agent decides it needs additional information and fetches it via tools or APIs. In tandem with this, the agent uses tool orchestration – coordinating multiple tools and AI calls – to achieve complex tasks. Both are crucial for handling the open-ended nature of video understanding.

For instance, consider a scenario: a video agent is monitoring a security camera feed and it spots an unfamiliar face. A static system might just say “Unknown person detected.” But an agent with dynamic retrieval could decide to call an entity search service or search a watchlist database. It essentially asks a follow-up question: “Who is this person? Let me retrieve context about them.” If connected to the right tool, it might return, “This person appears to be John Doe, an employee, last seen on camera 3 days ago.” Now the agent has enriched its context with external knowledge. In a sense, it extended the context beyond what was initially available in the video.

Source: https://www.twelvelabs.io/blog/video-intelligence-is-going-agentic

Our current video agent framework Jockey is built around this principle of active tool use. Jockey uses a planner-worker-reflector architecture, where the planner can decide which tools to invoke at each step. In a video pipeline, tools could include: semantic video search with Marengo, video summarization with Pegasus, and clip trimming & concatenation with ffmpeg. The orchestrator (planner) essentially says, “Given the user’s goal or the current subtask, what context do I lack and which tool can fetch it?” This is similar to how modern LLM agent frameworks like Letta or LangGraph treat tools – as extensions of the context that can be pulled in dynamically.

All this dynamic retrieval and tooling must then be integrated back into the agent’s context window. The information from tools becomes part of the prompt (usually in a structured way). One of the key design patterns from the LLM agent world is tool augmentation with memory: every time a tool returns something, that result is added to the conversation context for the model to consider going forward. This creates a loop where the agent’s knowledge grows step by step.

Adapted from: https://lilianweng.github.io/posts/2023-06-23-agent/

In short, dynamic retrieval and tool use turn a video AI system from a passive answerer into an active problem-solver. It ensures that if something isn’t in the immediate context, the system can go out and get it. The result is higher accuracy and versatility – fewer “I don’t know” responses and fewer hallucinations, because the agent can check its work. It’s an approach aligned with the latest in video agent research (as seen in works like Stanford’s “VideoAgent” which integrate search within video analysis and OmAgent’s multimodal RAG+reasoning technique). Twelve Labs is pushing this frontier by making video agents that are context-aware, tool-equipped, and adaptive.

2.3 - Structured Context Packaging

One of the most powerful yet sometimes overlooked strategies in context engineering is how you format the context. We touched on this earlier in “Write Context,” but it’s worth delving deeper: providing context in a structured, schema-driven format can massively improve an agent’s performance, especially for complex video data. Instead of dumping free-form notes into the prompt, we package context in a way that’s both concise and unambiguous.

Think of the difference between these two prompts to Pegasus:

1. Unstructured: “Question: What happened at 2:15? Answer:”

2. Structured (JSON): {"scene": "02:15-02:45", "characters": ["Alice", "Bob"], "actions": ["Alice enters the room", "Bob looks surprised"], "question": "What happened at 2:15?"}

In the structured version, Pegasus doesn’t have to guess what parts of the input are context versus the actual question – it’s clearly labeled. It also gets important information (characters, actions) up front in a compressed form. This reduces the cognitive load on the model and guides it to the answer. As an industry best practice, using structured formats (like JSON with clear fields) and including metadata (like timestamps or speaker labels) is highly effective. It gives the model additional signals for reasoning and helps ground its responses.

For TwelveLabs, structured packaging is a natural fit because video data is inherently structured (by time, by modality). We often represent context as timelines, lists, or maps:

• A timeline of events in the video (with timecodes and descriptions).

• A list of detected objects or people in a scene.

• A map of dialogue turns (who spoke when).

• A set of tags or vector IDs for retrieved clips.

By sending this kind of data structure, we essentially provide the model with an outline or knowledge graph rather than a raw blob of text. This can dramatically improve accuracy. For example, when we ask Pegasus to generate a summary of a video, we might first feed it a structured breakdown of the video’s scenes. This way, Pegasus “knows” the video’s segmented context and can ensure it covers each important part in the final summary. It’s akin to giving an essay outline to a writer.

Another advantage is controlling output via structured input. If the model needs to output a certain format (say, JSON of events), giving it input context in a similar structured way sets the expectation. In our agent Jockey’s interface, we often display results with timestamps and thumbnails; behind the scenes, Jockey’s reasoning included structured context so it could easily reference “timestamp”: value pairs.

In summary, structured context packaging is about being explicit and efficient. Explicit, in that we spell out the role of each piece of information (no guessing for the model). Efficient, in that we often compress context by turning it into a data structure (removing redundancy and focusing on key fields). It’s a production-grade technique: seasoned AI engineers treat context assembly almost like designing an API contract for the model. We decide what fields to include, how to name them, how to order them – all to maximize the model’s comprehension. TwelveLabs bakes this philosophy into our products, enabling developers to shape video context in structured ways rather than leaving it as a wild tangle of text.

3 - Applications and Future Directions

3.1 - Applications of Context-Centric Video AI

The techniques we’ve discussed aren’t just academic—they’re enabling real-world breakthroughs in video AI across industries. But context isn’t a one-size-fits-all solution; it’s use-case specific, shaped by why you want the system to perceive or act in a certain way. There’s no “perfect” context—there’s context that makes sense for your task. At Twelve Labs, we recognize this: whether it's media production, public safety, or advertising, we engineer context around use-case goals, not around generic completeness. In the examples that follow, you’ll see how context engineering is tailored to practical aims and why that tailoring will define next-generation video AI platforms—not just raw model scale or clever prompting, but strategic, task-aligned context design.

Media & Entertainment

Let’s revisit the sports highlight reel, because it demonstrates how context must merge both technical understanding of the domain and narrative awareness of what the user intends. In the case of the major sports franchise (MLSE), our agentic video system transformed a 16-hour manual editing workflow into a 9‑minute automated process by combining technical context (game structure, player metadata, timestamps) with narrative context (the desired storyline or editorial direction provided by the user). The system didn’t just detect moments—it understood what should go in the reel and in what order, based on the user’s creative input and the dynamics of the game itself.

And this goes beyond just sports. Imagine using the same approach for movie trailers, news montages, or TikTok-style recaps of long videos. The key isn’t just knowing “what matters” in the footage —it’s understanding why it matters for the output you're creating. That is, context engineering must be a response to the question: “What are we trying to achieve with this content?” Only then can the AI enforce narrative consistency—whether that’s covering the key plot points in the right order, ensuring factual accuracy with citations and timestamps, or matching the tone and pacing requested by a creative brief.

Media companies are also exploring multimodal search – finding that one scene across a vast archive where, say, a particular phrase was said and a certain action happened. With video-native context retrieval, that becomes feasible (no more tagging clips by hand endlessly).

Public Safety & Security

Consider the challenge of monitoring dozens of CCTV cameras in a city for specific incidents. Context-engineered video AI can act as a tireless observer with a perfect memory. Because it can retain long-term context, it can recognize that the same individual has appeared at multiple locations over days (flagging a possible stalker or a missing person sighting). Because it can orchestrate tools and retrieval, it can cross-reference faces with watchlists or vehicle license plates with databases in real time. For example, an alert might say, “Person in red jacket seen leaving a package unattended at 3pm; this same person was near a train station camera 2 hours ago.”

The video AI assembled that context from multiple feeds and external data (like a known suspects list) dynamically. Public safety agencies are piloting systems where an AI assistant helps dispatchers by summarizing evolving situations from live video (e.g., “Camera 5: crowd gathering, appears to be a protest forming”). The trust comes from transparency – the AI can show exactly the clips that led to its summary, so officials can verify and act. This level of situational awareness, powered by context engineering, means faster response times and possibly lives saved.

Advertising & Marketing

In advertising, context is king – placing the right ad in the right context can double engagement. Video AI can analyze content to an uncanny degree: not just “this is a cooking video” but “this video’s tone is nostalgic and it features outdoor family scenes.” Such context understanding allows matching ads that resonate emotionally or thematically (maybe a family car ad would fit well here).

Moreover, brands can use video AI to generate content: for instance, automatically create short social media clips from a long commercial shoot, each tailored to highlight a different product feature. An agent like Jockey could take a 30-minute product demo video and cut it into a series of 30-second thematic clips (one focusing on design, one on performance, etc.), using context cues to know where each theme appears.

In marketing analytics, you could have AI watch all your competitor’s YouTube ads and summarize the key messages and visuals, giving you a report – something currently done by human interns painstakingly. With context-engineered video understanding, the AI can even output structured data: e.g., a JSON of “brand logo appeared at these timestamps, slogan spoken here, product shown here” for every video analyzed. This structured context can feed into strategy decisions.

In short, the next wave of advertising will heavily involve AI that truly watches and comprehends content, enabling both smarter ad placement and automated content creation at scale.

These examples only scratch the surface. Other domains include education (e.g., personalized video lessons assembled by AI tutors who know what a student has learned before – context from past sessions), healthcare (analyzing procedure videos to provide surgeons with guidance, with context awareness of patient data), and legal (rapidly scanning hours of deposition footage for key moments or inconsistencies, maintaining context across a case’s video evidence).

3.2 - The Future of Multimodal Intelligence

Looking ahead, the future of multimodal video intelligence is incredibly exciting. We foresee:

• Agents that anticipate needs (Flow-aware agents): Much like a good human assistant, video agents will use flow-aware planning to predict what you might ask next or need next. For example, if you’re making a highlight reel, the agent might proactively start gathering context for the next likely clip while you review the current one. This requires contextual meta-learning – learning your preferences and habits – which is an extension of long-term memory. Over time, the agent adapts: it learns what you consider a “highlight” and tailors context retrieval accordingly.

• Deeper integration of modalities (Multimodal orchestration): Future video AI will seamlessly blend text, audio, visual, and even generated media. An agent might detect an important event in video, use text context to reason about it, and then generate a short video summary with voice-over for you. This means the context includes not just existing data but also generated context (like a synthesized voice explanation of a silent CCTV clip). Orchestration might involve creating new visuals from context (like “zoom in and enhance this detail” – generating a high-res image from a low-res frame using a super-resolution model). The agent essentially becomes a director orchestrating multiple AI “actors” – and context engineering is the script that ensures they all work in concert.

• Higher-order reasoning and self-reflection: As context systems mature, agents will get better at reflecting on their context assembly process. They might ask themselves: “Do I have enough information? Is my context possibly misleading or missing something?” For example, an agent might flag, “I have summarized this video, but I am not confident because the scene was chaotic – do you want to review that part?” This kind of self-awareness in agents can further build trust, as the agent knows the limits of its context. Technically, this could involve the agent using an LLM to critique its own output against the context, or to request more context if uncertain. We see early signs of this in research (like SelfCheckGPT for text), and it will likely come to video agents as well.

Finally, why do we say context engineering will be the defining capability of next-gen video AI? Because models are becoming commoditized – given the increasing performance of open-source models and the decreasing cost of closed-sourced APIs. The real competition will be in how effectively one can use them. This is a durable advantage: it’s easier for a competitor to spin up a new model than to replicate a refined pipeline full of proprietary context (your data, your workflows, years of optimization). TwelveLabs recognizes this; that’s why we’re building category-defining tools for your video understanding applications – tools that let you harness these pillars and strategies out of the box. We want developers to spend their time innovating on applications, not reinventing context management from scratch. A good start is to check out our MCP server.

Conclusion

Video understanding isn’t solved by just throwing a huge model at raw pixels. It’s solved by engineering the context around those pixels – writing down what matters, selecting the right pieces at the right time, compressing intelligently, and isolating information for clarity. It’s solved by having memory, by actively retrieving and using tools, and by structuring data for maximum clarity. It’s reinforced by measuring everything so we can trust and continually improve the system. This is how we turn the deluge of video data from a headache into an opportunity.

At TwelveLabs, by focusing on context, we aim to make video AI actually work for the people building the future – from researchers pushing the boundaries to ML infrastructure engineers scaling these solutions in production. Context engineering for video is our guiding star, and we believe it will light the way for the next era of video intelligence.

Thanks to my TwelveLabs colleagues (Ryan Khurana, Jin-Tan Ruan, and Yoon Kim) for comments, feedback, and suggestions that helped with this post. Huge thanks to Sean Barclay and Jieyi Lee for the amazing visuals that accompany the piece.