{"id":2080,"date":"2025-07-22T08:49:39","date_gmt":"2025-07-22T08:49:39","guid":{"rendered":"https:\/\/yodaplus.com\/blog\/?p=2080"},"modified":"2025-07-23T08:55:02","modified_gmt":"2025-07-23T08:55:02","slug":"how-artificial-intelligence-agents-use-text-image-and-table-memory","status":"publish","type":"post","link":"https:\/\/yodaplus.com\/blog\/how-artificial-intelligence-agents-use-text-image-and-table-memory\/","title":{"rendered":"How Artificial Intelligence Agents Use Text, Image, and Table Memory"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">As <\/span><a href=\"https:\/\/bit.ly\/4iCygh5\"><span style=\"font-weight: 400;\">Artificial Intelligence (AI)<\/span><\/a><span style=\"font-weight: 400;\"> advances, we are now seeing a new type of system called multimodal agents. These agents can work with different types of data, such as images, text, and tables. This helps them understand tasks more clearly and respond more accurately.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Whether it&#8217;s in logistics, finance, or customer support, the ability to use multiple data formats is changing how AI agents operate. In this blog, we\u2019ll explain how these agents are designed, how their memory works, and how technologies like Agentic AI, LLMs, and machine learning support them.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>What Are Multimodal Agents?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Multimodal agents are <\/span><a href=\"https:\/\/bit.ly\/3GaKQFO\"><span style=\"font-weight: 400;\">autonomous agents<\/span><\/a><span style=\"font-weight: 400;\"> that can read and understand more than just text. They can work with:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Text<\/b><span style=\"font-weight: 400;\"> like reports, emails, or instructions<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Images<\/b><span style=\"font-weight: 400;\"> such as diagrams, photos, or screenshots<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Tables<\/b><span style=\"font-weight: 400;\"> like spreadsheets or structured data files<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Many traditional AI systems rely only on text using natural language processing (NLP). But multimodal agents bring together different types of information, which helps them handle complex tasks more effectively.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>Why Memory Matters in Agentic AI<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A key feature of multimodal agents is memory. These agents can remember and use information across different steps of a task. This is important when the work involves more than one piece of data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are three main types of memory:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Short-term memory<\/b><span style=\"font-weight: 400;\"> stores information for a single task.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Long-term memory<\/b><span style=\"font-weight: 400;\"> keeps important knowledge for future use.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Multimodal memory<\/b><span style=\"font-weight: 400;\"> handles images, tables, and text all together.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Systems like MCP (Model Context Protocol) help agents manage and share this memory. In an agentic framework, different agents may work together, using shared memory to stay in sync and complete tasks more efficiently.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>Why Image, Text, and Table Memory Are Important<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Imagine a <\/span><a href=\"https:\/\/bit.ly\/4j1lj0y\"><span style=\"font-weight: 400;\">Crew AI<\/span><\/a><span style=\"font-weight: 400;\"> agent helping with ship maintenance. It might need to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Read a manual or checklist (text)<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Review equipment photos (image)<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Check a maintenance log or fuel report (table)<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Without memory, the agent would keep asking for the same information. But with multimodal memory, the agent can remember recent actions, spot problems in images, and compare data across formats. This improves both speed and accuracy.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>What Powers a Multimodal Agent?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Here are the core parts of a multimodal AI agent:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Memory Storage<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> This stores the agent&#8217;s memory. Many systems use <\/span><a href=\"https:\/\/bit.ly\/3HbQsAb\"><span style=\"font-weight: 400;\">LLMs<\/span><\/a><span style=\"font-weight: 400;\"> to handle text, images, and tables in one shared space.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Smart Retrieval<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> With techniques like RAG (Retrieval-Augmented Generation), agents can look up past data before replying.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Context Handling with MCP<\/b><b><br \/>\n<\/b> <b>MCP<\/b><span style=\"font-weight: 400;\"> helps agents keep track of conversations and pass information between each other during a workflow.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use of Generative AI<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Generative models help produce answers, summaries, or visual feedback using <\/span><b>machine learning<\/b><span style=\"font-weight: 400;\">.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3><b>Where Multimodal Agents Are Used<\/b><\/h3>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Healthcare<\/b><span style=\"font-weight: 400;\">: Agents can check patient records, review scans, and track vitals.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Finance<\/b><span style=\"font-weight: 400;\">: AI can pull insights from reports, tables, and charts.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Maritime<\/b><span style=\"font-weight: 400;\">: Systems like Crew AI can read SOPs, inspect images, and use HSEQ tables.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Legal<\/b><span style=\"font-weight: 400;\">: AI helps read case files, scanned documents, and compliance sheets.<\/span><span style=\"font-weight: 400;\">\n<p><\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">These agents work better when they use multiple data types, not just text. That\u2019s what makes Artificial Intelligence solutions more practical in real-world jobs.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>How to Design a Multimodal Agent<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">If you\u2019re building one, follow these steps:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Choose the Data Types<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Decide if the agent needs to work with text, tables, images, or all of them.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Create a Memory System<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Use tools like vector databases to store information in a way that\u2019s easy to search.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pick the Right Framework<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Use an <\/span><b>agentic framework<\/b><span style=\"font-weight: 400;\"> that supports teamwork between agents and memory sharing. MCP is a good example.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Train Your Agent<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Use <\/span><b>machine learning<\/b><span style=\"font-weight: 400;\"> to train the agent on your specific tasks or industry data.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Test the Workflow<\/b><b><br \/>\n<\/b><span style=\"font-weight: 400;\"> Make sure the agent retrieves the right memory, shares it properly, and gives helpful results.<\/span><span style=\"font-weight: 400;\"><\/p>\n<p><\/span><\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<h3><b>Final Thoughts<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Multimodal agents are changing how we use AI technology. By working with images, text, and tables, they create a fuller understanding of the task. This leads to better performance and smarter results.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If your data is stored in different formats, it may be time to explore <\/span><a href=\"https:\/\/bit.ly\/4cm5MWk\"><span style=\"font-weight: 400;\">Agentic AI<\/span><\/a><span style=\"font-weight: 400;\">. These systems can read, remember, and respond with more context. Whether it\u2019s Crew AI in shipping, legal automation, or financial tools, agents that use multimodal memory are becoming essential.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">At <\/span><a href=\"https:\/\/bit.ly\/3XdzxCr\"><span style=\"font-weight: 400;\">Yodaplus<\/span><\/a><span style=\"font-weight: 400;\">, we help businesses build intelligent, context-aware systems using AI, memory, and multimodal capabilities. Autonomous systems are not just about automation\u2014they\u2019re about understanding. And with the right memory support, they can do much more than just follow instructions.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As Artificial Intelligence (AI) advances, we are now seeing a new type of system called multimodal agents. These agents can work with different types of data, such as images, text, and tables. This helps them understand tasks more clearly and respond more accurately. Whether it&#8217;s in logistics, finance, or customer support, the ability to use [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2081,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[86,49],"tags":[],"class_list":["post-2080","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agentic-ai","category-artificial-intelligence"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How Artificial Intelligence Agents Use Text, Image, and Table Memory | Yodaplus Technologies<\/title>\n<meta name=\"description\" content=\"Speed up ship inspections with AI-powered document intelligence for shipping documents, HSEQ, and maritime compliance.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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