--- title: "Multimodal Data for AI Agents: Text, Images, and Video" url: "https://atlan.com/know/ai-agent/data-for-ai/multimodal-data-for-ai-agents/" description: "Learn how enterprises handle text, images, and video for AI agents using metadata, lineage, multimodal governance, and shared context layers." author: "Emily Winks" author_role: "Data Governance Expert" published: "2026-07-17" updated: "2026-07-17T00:00:00.000Z" --- --- A support agent retrieves a deployment diagram that looks exactly right, and it's exactly wrong: the architecture it shows was deprecated two releases ago. The embedding matched perfectly; nothing about the image told the agent it was stale. According to IBM, [90% of enterprise data is unstructured](https://www.ibm.com/think/topics/structured-vs-unstructured-data) and growing three times faster than structured data. Atlan's context layer extends the same governance already applied to structured data, ownership, [lineage](https://atlan.com/know/metadata-layer-for-ai/), freshness, and access policy, to every modality an agent can reach, screenshots, recordings, diagrams, and [unstructured transcripts](https://atlan.com/know/data-for-ai/unstructured-data-for-ai/) included, closing a gap that similarity-only [vector search](https://atlan.com/know/vector-database-vs-knowledge-graph-agent-memory/) leaves wide open. --- | Area | Enterprise reality | | :---- | :---- | | **Enterprise data composition** | Most enterprise AI context now originates from unstructured multimodal assets rather than relational systems | | **Biggest operational risk** | Cross-modal inconsistency between documents, screenshots, transcripts, and video context | | **Most overlooked issue** | Visual freshness governance for recordings, diagrams, dashboards, and embedded screenshots | | **Core retrieval problem** | Missing metadata relationships between modalities: ownership, timestamps, and lineage | | **Most important architecture shift** | Moving from similarity-only retrieval to shared context graphs with governed semantic relationships | --- ## Why is multimodal enterprise data difficult to handle at scale? Text systems matured around governance long before AI agents arrived. Your documents already carry ownership, timestamps, lineage, and freshness policies across databases, BI systems, and document platforms. Visual systems evolved differently. Screenshots, demo recordings, CAD diagrams, and support videos often move through enterprise systems without owners, [semantic relationships](https://atlan.com/know/ai-agent/semantic-layer-for-ai-agents/), freshness metadata, or lifecycle governance. A document usually has a version history; a screenshot usually doesn't. This is why multimodal AI becomes a context problem before it becomes a model problem: enterprise AI doesn't fail because multimodal models are weak, it fails because governance practices built for structured and text systems never extended to visual and media assets, the same [organizational cold start](https://atlan.com/know/ai-agent-cold-start-problem/) that shows up whenever a new modality joins an agent's data surface. The data stack is shifting under AI See the 7 shifts reshaping data infrastructure for an AI-first world, including why visual assets need the same governance as structured data. Download the 2026 Report ### Why do images and video create different governance problems than text? Text carries explicit structure; images and video carry implicit meaning. A contract usually has an owner, version history, and approval trail. A product screenshot inside Slack often has none of them. The business context lives inside pixels, waveforms, and timestamps that AI agents can't reliably interpret without [metadata enrichment](https://atlan.com/know/data-for-ai/data-quality-for-ai-agent/), the same gap that shows up whenever [agent and human data discovery](https://atlan.com/know/ai-agents-vs-humans-data-discovery/) diverge. This creates a semantic density problem. A 10-minute demo recording may simultaneously include UI changes, spoken decisions, and policy exceptions. None of it becomes retrievable until your systems convert visual and audio signals into governed metadata, [lineage relationships](https://atlan.com/know/context-infrastructure-for-ai-agents/), and searchable context, the [institutional knowledge](https://atlan.com/know/data-for-ai/institutional-knowledge-loss/) locked inside media that never gets captured any other way. ### Why does multimodal scale increase retrieval complexity? The retrieval challenge compounds as modalities scale across petabyte-sized media archives, vector indexes, and object storage. [Statista projects global data creation will exceed 394 zettabytes by 2028](https://www.statista.com/topics/1464/big-data/), with most enterprise growth concentrated in unstructured and multimodal content, while research on web-crawled multimodal datasets documents inevitable noise, mismatched pairs, and degraded modalities that measurably hurt model performance. Embeddings retrieve similarity; metadata establishes trustworthiness. That distinction matters when your agent encounters missing video frames, corrupted feeds, or stale screenshots during retrieval. Without shared context relationships, the system retrieves media that looks relevant but no longer reflects operational reality. --- ## Why do multimodal AI agents fail in production? Production failures begin when AI agents can't distinguish operational reality from stale multimodal context. The models are usually not the problem: modern multimodal systems already interpret screenshots, diagrams, transcripts, and recordings effectively. Failures emerge later, when retrieval systems can't determine which modality reflects the current source of truth. Consider a product-support agent retrieving specifications, architecture diagrams, screenshots, and demo recordings during troubleshooting: | Failure type | What breaks | | :---- | :---- | | **Staleness** | Retrieved diagram reflects the deprecated deployment architecture | | **Governance leakage** | Support recording exposes visible customer identifiers | | **Cross-modal inconsistency** | Documentation, diagrams, and videos describe conflicting workflows | Trustworthiness breaks before retrieval breaks. ### Why does cross-modal inconsistency confuse AI agents? Cross-modal inconsistency appears when connected modalities evolve independently, lacking shared lineage relationships. A text specification may reference the latest workflow while a linked demo recording still reflects an older release branch; without lineage, the agent can't determine authoritative truth. Enterprise retrieval systems rely on linked spec IDs, source lineage, version relationships, and temporal weighting to prioritize operationally correct assets over semantically similar but outdated media. Visual assets create additional governance complexity because they rarely participate in freshness governance workflows: a deprecated architecture diagram causes incorrect dependency resolution; a legacy UI recording causes invalid workflow guidance; an old dashboard screenshot shows outdated operational state. Governance fragmentation also increases at the boundaries between modalities. [Gartner predicts that by 2028, 40% of CIOs will demand "Guardian Agents"](https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure) to autonomously oversee AI agent actions, reinforcing that multimodal systems need context-aware governance rather than uniform policy enforcement. A transcript may inherit sensitivity classifications correctly while linked screenshots remain unclassified, creating policy violations across connected modalities, precisely the kind of exposure [GDPR compliance for AI agents](https://atlan.com/know/ai-agent/gdpr-compliance-for-ai-agents/) requires you to catch before an agent ever surfaces the asset. Modern multimodal systems address this through multimodal role-based access control, policy inheritance, lineage-aware governance, and freshness-aware retrieval. According to an independent 2026 study on metadata-enriched retrieval, ranking assets on governance and lineage context rather than similarity alone improved retrieval accuracy from 33% to 55%. Where's your multimodal governance gap? Run the Context Gap Calculator to see which of your visual and media assets are still ungoverned. Calculate Your Gap --- ## How do enterprises handle multimodal data reliably for AI agents? Reliable multimodal systems operate on one shared context substrate across text, images, video, transcripts, and metadata, the same [memory layer](https://atlan.com/know/memory-layer-for-ai-agents/) discipline that keeps a single agent's context coherent across sessions. Instead of maintaining isolated retrieval pipelines per modality, enterprises are shifting toward relationship-aware architectures, built on [knowledge graphs](https://atlan.com/know/ai-agent/knowledge-graph-for-ai-agents/) rather than flat vector indexes, that preserve lineage, governance, and freshness across connected assets. ### How do context graphs connect text, images, and video? [Context graphs](https://atlan.com/know/context-graph-vs-knowledge-graph/) preserve relationships that embeddings alone can't capture. A product specification may connect directly to deployment screenshots, architecture diagrams, and demo recordings; a support transcript may link to troubleshooting videos and incident timelines. Instead of retrieving isolated assets independently, the system traverses the connected enterprise context across modalities. | Connected asset | Relationship preserved through context graphs | | :---- | :---- | | **Product spec to architecture diagram** | Version dependency | | **Support ticket to demo recording** | Workflow validation | | **Dashboard capture to incident transcript** | Operational context | | **Screenshot to release documentation** | Temporal alignment | This shared [context layer](https://atlan.com/know/context-layer-enterprise-ai/) changes how retrieval ranking works. Modern multimodal systems combine metadata-enriched embeddings with trust-aware and freshness-aware retrieval to prioritize operationally reliable assets over semantically similar but outdated media. If a deployment diagram conflicts with the latest spec, lineage relationships help the system trace upstream ownership and release chronology before ranking authoritative truth. --- ## What architecture patterns make multimodal AI trustworthy? Reliable multimodal systems rely on shared governance and context frameworks across text, images, video, transcripts, and metadata, instead of treating each modality as a separate retrieval problem. | Traditional multimodal retrieval | Governed multimodal context | | :---- | :---- | | Similarity-only retrieval | Metadata-enriched retrieval | | Isolated modality pipelines | Shared context graph | | No freshness governance | Freshness SLAs | | Static ranking | Trust-aware retrieval | | Manual policy enforcement | Policy propagation | ### What governance controls apply to visual assets? Screenshots, recordings, diagrams, and dashboard captures participate in the same governance controls applied to structured systems: ownership and stewardship, [lineage relationships](https://atlan.com/know/what-are-decision-traces-for-ai-agents/), freshness policies, sensitivity classification, and retention enforcement. The structural change that makes visual assets governable isn't tagging them after the fact; it's extending the same frameworks that already govern your structured data estate to cover every modality an agent can reach. ### How does trust-aware retrieval differ from similarity-only retrieval? Similarity-only retrieval ranks assets by semantic proximity. Trust-aware retrieval ranks them by governance relationships, temporal validity, and operational trust signals across modalities: the system no longer retrieves what appears relevant, it retrieves what remains operationally valid. Retrieval infrastructure is maturing faster than governance infrastructure, and that gap determines whether [multimodal AI systems](https://atlan.com/know/context-aware-ai-agents/) remain trustworthy in production. --- ## How Atlan approaches multimodal context governance ### The challenge Multimodal AI systems often fail because governance remains fragmented across documents, media archives, vector indexes, and policy systems. Your agents retrieve connected modalities, but [governance workflows](https://atlan.com/know/ai-agent-memory-governance/) still operate in silos, exactly the pattern [AI agent governance](https://atlan.com/know/ai-agent-governance/) frameworks exist to close. ### The approach Atlan connects text, images, video, dashboards, transcripts, and structured assets through one unified metadata and context layer with shared governance relationships: | Challenge | Atlan approach | Operational outcome | | :---- | :---- | :---- | | **Disconnected modality pipelines** | Unified metadata and context layer | Shared context across connected assets | | **Missing cross-modal relationships** | Cross-modal lineage tracking | More authoritative multimodal retrieval | | **Inconsistent policy enforcement** | Policy propagation across modalities | Reduced governance fragmentation | | **Stale visual assets** | Freshness governance and temporal metadata | Trust-aware retrieval prioritization | | **Fragmented AI context delivery** | MCP-based governed context delivery | Retrieval-ready enterprise context | ### The outcome AI agents retrieve multimodal enterprise context with lineage awareness, freshness validation, and governance continuity. The [Atlan MCP server](https://atlan.com/know/mcp/why-mcp-matters-for-ai-agents/) delivers this governed context to any MCP-compatible agent at inference time, and **Context Engineering Studio** bootstraps the initial context layer from existing signals so teams don't start from scratch when [implementing an enterprise context layer](https://atlan.com/know/how-to-implement-enterprise-context-layer-for-ai/) that extends to new modalities. The goal isn't better embeddings or larger models; it's making [multimodal enterprise context](https://atlan.com/know/enterprise-context-layer/) trustworthy, connected, and operationally reliable at scale. --- ## Real stories from real customers: governing multimodal context at scale "When you're working with AI, you need contextual data to interpret transactional data at the speed of transaction. We chose Atlan, a platform that's configurable, intuitive, and able to scale with our 100M+ data assets." Andrew Reiskind, Chief Data Officer, Mastercard Watch Now "Atlan is much more than a catalog of catalogs. It's more of a context operating system. Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models." Sridher Arumugham, Chief Data & Analytics Officer, DigiKey Watch Now Mastercard's scale, hundreds of millions of assets governed through [Atlan's metadata lakehouse](https://atlan.com/know/atlan-context-layer-enterprise-memory/), is the context infrastructure discipline multimodal governance at enterprise scale requires: governance that works at that scale works across all modalities. DigiKey's unification of six systems into one governed context layer, spanning product data, technical documentation, and catalog assets across formats, is the practical implementation of the [enterprise context layer](https://atlan.com/know/why-ai-agents-need-an-enterprise-context-layer/) case for cross-modality governance. What's the ROI of trust-aware retrieval? Run the Context Layer ROI Calculator to size the cost of agents retrieving stale visual assets. Calculate the ROI --- ## Building reliable multimodal AI systems at enterprise scale The next enterprise AI bottleneck isn't multimodal understanding. Modern models already interpret screenshots, diagrams, transcripts, and recordings effectively. The harder problem is maintaining trust across modalities at scale: as retrieval systems expand across media archives, vector indexes, and operational workflows, enterprises need governance models that preserve freshness, lineage, ownership, and contextual integrity. Embeddings alone can't resolve authoritative truth across evolving enterprise systems. The enterprises building the most reliable AI agents aren't simply indexing more multimodal data. They're treating images, video, audio, and transcripts as first-class governed assets connected through [shared context relationships](https://atlan.com/know/enterprise-ai-memory-layer/). [Context agents](https://atlan.com/context-agents/) built on this governed layer operate with lineage awareness and access policy enforcement from the first query. The next generation of reliable AI systems will be defined by how well enterprise context remains governed, connected, and trustworthy across modalities, not by model capability alone. Book a Demo --- ## FAQs about multimodal data for AI agents ### 1. Why do multimodal AI agents fail more often in production than in demos? Production systems introduce stale media, fragmented governance, inconsistent lineage, and conflicting modalities that rarely appear in controlled demo environments. Retrieval reliability degrades when operational context loses its freshness and continuity of trust across systems. ### 2. Why are screenshots and videos harder to govern than documents? Documents usually inherit ownership, version history, and retention policies automatically. Screenshots, recordings, and diagrams often move across enterprise systems without freshness metadata, lineage tracking, or sensitivity classification. ### 3. Can embeddings alone support enterprise multimodal retrieval? No. Embeddings retrieve semantic proximity but can't independently determine freshness, source authority, policy validity, or operational trustworthiness across connected modalities. ### 4. What role does lineage play in multimodal AI systems? Lineage helps retrieval systems trace upstream ownership, downstream dependencies, release chronology, and authoritative source relationships when modalities contain conflicting operational context. ### 5. What makes multimodal retrieval trustworthy at enterprise scale? Reliable multimodal retrieval depends on freshness-aware ranking, governance-aware retrieval, cross-modal relationships, and policy propagation rather than similarity matching alone. ### 6. Why are enterprises moving toward shared context architectures? Shared context architectures preserve governance, lineage, freshness, and trust relationships across text, image, video, and transcript systems. This reduces disconnected retrieval across isolated modality pipelines and prevents the multi-agent memory silo problem from compounding at modality scale. --- ## Sources 1. [Structured vs. Unstructured Data, IBM](https://www.ibm.com/think/topics/structured-vs-unstructured-data) 2. [Global Data Volume 2010-2028, Statista (2024)](https://www.statista.com/topics/1464/big-data/) 3. [Gartner Says Applying Uniform Governance Across AI Agents Will Lead to Enterprise AI Agent Failure, Gartner (2026)](https://www.gartner.com/en/newsroom/press-releases/2026-05-26-gartner-says-applying-uniform-governance-across-ai-agents-will-lead-to-enterprise-ai-agent-failure)