{"id":7061423,"date":"2026-09-19T21:10:21","date_gmt":"2026-09-19T21:10:21","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/the-evolution-of-ai-operations\/"},"modified":"2026-09-19T21:10:21","modified_gmt":"2026-09-19T21:10:21","slug":"the-evolution-of-ai-operations","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=7061423","title":{"rendered":"The Evolution of AI Operations"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"article-start\">\n<p class=\"wp-block-paragraph\">Putting AI into production now\u00a0takes\u00a0more than deploying\u00a0a model\u00a0and tracking accuracy.\u00a0MLOps made traditional\u00a0ML\u00a0manageable, while LLMOps\u00a0added\u00a0concerns\u00a0around prompts, retrieval, evaluation, latency, and cost.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">AgentOps\u00a0adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams\u00a0monitor\u00a0and control. In this article, we compare\u00a0MLOps,\u00a0LLMOps, and\u00a0AgentOps, and explain how observability evolves as AI systems move\u00a0to action.\u00a0<\/p>\n<h2 id=\"h-what-is-mlops\" class=\"wp-block-heading\">What Is\u00a0MLOps?<\/h2>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img fetchpriority=\"high\" decoding=\"async\" width=\"660\" height=\"474\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-78.png\" alt=\"MLOps\" class=\"wp-image-257624\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-78.png 660w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-78-300x215.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image-78-150x108.png 150w\" sizes=\"(max-width: 660px) 100vw, 660px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">MLOps\u00a0stands for Machine Learning Operations.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It covers the practices used to build, deploy,\u00a0monitor, and\u00a0maintain\u00a0machine learning models in production. The goal is to make ML systems reliable, repeatable, and easier to manage at\u00a0a scale.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A typical\u00a0MLOps\u00a0workflow includes:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li>Data collection and validation\u00a0<\/li>\n<li>Model training\u00a0<\/li>\n<li>Experiment tracking\u00a0<\/li>\n<li>Model versioning\u00a0<\/li>\n<li>Deployment\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">For example, a fraud detection model may be trained on historical transactions, deployed as an API, and\u00a0monitored\u00a0for accuracy and data\u00a0drift.\u00a0If\u00a0performance drops, the model may need\u00a0retraining.\u00a0MLOps\u00a0is\u00a0mainly built\u00a0around predictive models where the output is usually structured, measurable, and easier to compare against a known target.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">Read more:\u00a0<a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/12\/mlops-operations-a-guide-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\">MLOPs Operations: A Beginner\u2019s Guide in Python<\/a>\u00a0<\/p>\n<h2 id=\"h-what-is-llmops\" class=\"wp-block-heading\">What Is\u00a0LLMOps?<\/h2>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1158\" height=\"854\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-8.png\" alt=\"LLMOps\" class=\"wp-image-257615\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-8.png 1158w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-8-300x221.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-8-768x566.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image2-8-150x111.png 150w\" sizes=\"auto, (max-width: 1158px) 100vw, 1158px\"\/><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">LLMOps\u00a0stands for Large Language Model Operations.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">It focuses on deploying, monitoring, and improving applications built with large language\u00a0models.\u00a0Unlike\u00a0traditional ML systems, LLM applications often depend on more than the model itself. They may use prompts, vector databases, retrieval pipelines, external APIs, and guardrails.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A typical\u00a0LLMOps\u00a0workflow includes:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li>Prompt versioning\u00a0<\/li>\n<li>Model selection\u00a0<\/li>\n<li>Retrieval monitoring\u00a0<\/li>\n<li>Evaluation of generated responses\u00a0<\/li>\n<li>Token and cost tracking\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">For example, a customer support assistant may use an LLM with RAG to answer questions from company documents.\u00a0Here, teams need to\u00a0monitor\u00a0not just model performance, but also retrieval quality, response quality, token usage, and cost.\u00a0That makes\u00a0LLMOps\u00a0broader than traditional\u00a0MLOps\u00a0for generative AI systems.\u00a0<\/p>\n<h2 id=\"h-what-is-agentops\" class=\"wp-block-heading\">What Is\u00a0AgentOps?<\/h2>\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image3-7.png\" alt=\"AgentOps\" class=\"wp-image-257616\" srcset=\"https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image3-7.png 1536w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image3-7-300x200.png 300w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image3-7-768x512.png 768w, https:\/\/cdn.analyticsvidhya.com\/wp-content\/uploads\/2026\/09\/image3-7-150x100.png 150w\" sizes=\"auto, (max-width: 1536px) 100vw, 1536px\"\/><\/figure>\n<p class=\"wp-block-paragraph\">AgentOps\u00a0focuses on operating AI agents in production.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">An AI agent does more than generate a response. It can plan tasks, call tools, use memory, make decisions, and take actions across multiple\u00a0steps.\u00a0This creates new operational\u00a0challenges.\u00a0A typical\u00a0AgentOps\u00a0setup may track:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li>Agent decisions\u00a0<\/li>\n<li>Tool calls\u00a0<\/li>\n<li>Multi-step traces\u00a0<\/li>\n<li>Task completion rates\u00a0<\/li>\n<li>Failed actions\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">For example, an AI travel agent may\u00a0search\u00a0flights, compare options, check calendars, and create a booking request.\u00a0If something goes wrong, teams need to know which step failed and why.\u00a0AgentOps\u00a0therefore adds observability at the workflow level.\u00a0The focus is not only on what the model said, but also on what the agent did.\u00a0<\/p>\n<h2 id=\"h-mlops-vs-llmops-vs-agentops\" class=\"wp-block-heading\">MLOps\u00a0vs\u00a0LLMOps\u00a0vs\u00a0AgentOps<\/h2>\n<p class=\"wp-block-paragraph\">The main difference is what you are\u00a0operating.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">MLOps\u00a0focuses on machine learning models.\u00a0LLMOps\u00a0focuses on language model applications.\u00a0AgentOps\u00a0focuses on systems where AI agents take\u00a0actions\u00a0across multiple steps.\u00a0<\/p>\n<div style=\"\">\n<figure class=\"wp-block-table\">\n<table class=\"has-fixed-layout\" style=\"width: 100%; border-collapse: collapse;\">\n<tbody>\n<tr>\n<td style=\"border: 1px solid #d1d5db; background-color: #f3f4f6; padding: 10px;\"><strong>Area<\/strong><\/td>\n<td style=\"border: 1px solid #d1d5db; background-color: #f3f4f6; padding: 10px;\"><strong>MLOps<\/strong><\/td>\n<td style=\"border: 1px solid #d1d5db; background-color: #f3f4f6; padding: 10px;\"><strong>LLMOps<\/strong><\/td>\n<td style=\"border: 1px solid #d1d5db; background-color: #f3f4f6; padding: 10px;\"><strong>AgentOps<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Main Focus<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">ML models<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">LLM applications<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">AI agents<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Typical Output<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Prediction or score<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Generated response<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Action or completed task<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Key Monitoring<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Accuracy, drift<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Quality, latency, cost<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Traces, tools, decisions<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Common Components<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Data, model, pipeline<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Prompt, model, RAG<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Agent, tools, memory<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Main Risk<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Model degradation<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Hallucination or poor output<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Wrong or unsafe action<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Evaluation<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Metrics against labels<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">Response quality<\/td>\n<td style=\"border: 1px solid #d1d5db; padding: 10px;\">End-to-end task success<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">The progression is simple:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li>MLOps\u00a0manages\u00a0predictions.\u00a0<\/li>\n<li>LLMOps\u00a0manages\u00a0generations.\u00a0<\/li>\n<li>AgentOps\u00a0manages actions.\u00a0<\/li>\n<\/ul>\n<h2 id=\"h-where-each-one-fits\" class=\"wp-block-heading\">Where Each One Fits<\/h2>\n<p class=\"wp-block-paragraph\">MLOps,\u00a0LLMOps, and\u00a0AgentOps\u00a0are not competing approaches. They solve different operational\u00a0problems.\u00a0Use\u00a0MLOps\u00a0when the core system is based on traditional machine learning models.\u00a0Use\u00a0LLMOps\u00a0when the application depends on large language models, prompts, retrieval, and generated\u00a0responses.\u00a0Use\u00a0AgentOps\u00a0when the system includes AI agents that use tools, make decisions, and complete multi-step tasks.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">A simple way\u00a0to think about it is:\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>MLOps:<\/strong>\u00a0operate\u00a0models\u00a0<\/li>\n<li><strong>LLMOps:<\/strong>\u00a0operate\u00a0language model applications\u00a0<\/li>\n<li><strong>AgentOps:\u00a0<\/strong>operate\u00a0autonomous or semi-autonomous workflows\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">In practice, teams may use all three\u00a0together.\u00a0For example, an agentic application may use a traditional ML model for scoring, an LLM for reasoning, and an agent layer for taking\u00a0actions.\u00a0The\u00a0operational stack depends on the system architecture.\u00a0<\/p>\n<h2 id=\"h-conclusion\" class=\"wp-block-heading\">Conclusion<\/h2>\n<p class=\"wp-block-paragraph\">MLOps, LLMOps, and AgentOps show how AI operations have evolved as systems have become more capable. MLOps focuses on models and predictions, LLMOps expands that scope to prompts, retrieval, generated responses, latency, and cost, while AgentOps adds workflow-level visibility into how agents reason, use tools, and complete tasks.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The takeaway is clear: operating AI is no longer just about monitoring a model. It is about understanding the full system behind every output and action. As AI applications move from prediction to generation to autonomous workflows, teams that build strong observability, evaluation, cost control, and safety practices will be best prepared to operate them at scale.\u00a0<\/p>\n<h2 id=\"h-frequently-asked-questions\" class=\"wp-block-heading\">Frequently Asked Questions<\/h2>\n<div class=\"schema-faq wp-block-yoast-faq-block\">\n<div class=\"schema-faq-section\" id=\"faq-question-1789542106988\"><strong class=\"schema-faq-question\">Q1. What is the main difference between\u00a0MLOps,\u00a0LLMOps, and\u00a0AgentOps?\u00a0<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A.\u00a0MLOps\u00a0manages\u00a0predictions,\u00a0LLMOps\u00a0manages\u00a0generated responses, and\u00a0AgentOps\u00a0manages AI-driven actions.\u00a0<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1789542115686\"><strong class=\"schema-faq-question\">Q2. When should teams use\u00a0LLMOps?\u00a0<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A. Teams use\u00a0LLMOps\u00a0when applications depend on large language models, prompts, retrieval, response quality, latency, and cost tracking.\u00a0<\/p>\n<\/p><\/div>\n<div class=\"schema-faq-section\" id=\"faq-question-1789542123636\"><strong class=\"schema-faq-question\">Q3. Why\u00a0is\u00a0AgentOps\u00a0important?\u00a0<\/strong> <\/p>\n<p class=\"schema-faq-answer\">A.\u00a0AgentOps\u00a0helps teams\u00a0monitor\u00a0agent decisions, tool calls, task completion, failed actions, and workflow-level behavior.\u00a0<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<div class=\"border-top py-3 author-info my-4\">\n<div class=\"author-card d-flex align-items-center\">\n<div class=\"flex-shrink-0 overflow-hidden\">\n                                    <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/author\/janvikumari01\/\" class=\"text-decoration-none active-avatar\"><br \/>\n                                                                       <img decoding=\"async\" src=\"https:\/\/av-eks-lekhak.s3.amazonaws.com\/media\/lekhak-profile-images\/converted_image_ToTu2tx.webp\" width=\"48\" height=\"48\" alt=\"Janvi Kumari\" loading=\"lazy\" class=\"rounded-circle\"\/><br \/>\n                                                                <\/a>\n                                <\/div>\n<\/p><\/div>\n<p>Hi, I am Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<p><h4 class=\"fs-24 text-dark\">Login to continue reading and enjoy expert-curated content.<\/h4>\n<p>                        <button class=\"btn btn-primary mx-auto d-table\" data-bs-toggle=\"modal\" data-bs-target=\"#loginModal\" id=\"readMoreBtn\">Keep Reading for Free<\/button>\n                    <\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>Putting AI into production now\u00a0takes\u00a0more than deploying\u00a0a model\u00a0and tracking accuracy.\u00a0MLOps made traditional\u00a0ML\u00a0manageable, while LLMOps\u00a0added\u00a0concerns\u00a0around prompts, retrieval, evaluation, latency, and cost.\u00a0 AgentOps\u00a0adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams\u00a0monitor\u00a0and control. In this article, we compare\u00a0MLOps,\u00a0LLMOps, and\u00a0AgentOps, and explain how observability evolves as AI systems move\u00a0to action.\u00a0 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":7061424,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[7471,13276],"dealstore":[],"offerexpiration":[],"class_list":["post-7061423","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-evolution","tag-operations"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Evolution of AI Operations - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=7061423\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Evolution of AI Operations - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Putting AI into production now\u00a0takes\u00a0more than deploying\u00a0a model\u00a0and tracking accuracy.\u00a0MLOps made traditional\u00a0ML\u00a0manageable, while LLMOps\u00a0added\u00a0concerns\u00a0around prompts, retrieval, evaluation, latency, and cost.\u00a0 AgentOps\u00a0adds another layer for systems that decide, call tools, and complete multi-step tasks. These shifts change what teams\u00a0monitor\u00a0and control. In this article, we compare\u00a0MLOps,\u00a0LLMOps, and\u00a0AgentOps, and explain how observability evolves as AI systems move\u00a0to action.\u00a0 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fivemor.com\/?p=7061423\" \/>\n<meta property=\"og:site_name\" content=\"Som2ny Network\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-19T21:10:21+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/fivemor.com\/wp-content\/uploads\/2026\/09\/LLMOps-vs-MLOps-vs-AgentOps.png\" \/>\n\t<meta property=\"og:image:width\" content=\"873\" \/>\n\t<meta property=\"og:image:height\" content=\"471\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fivemor.com\/?p=7061423#article\",\"isPartOf\":{\"@id\":\"https:\/\/fivemor.com\/?p=7061423\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/fivemor.com\/#\/schema\/person\/b85e3c3dc0e1daea076524dc8810c371\"},\"headline\":\"The Evolution of AI Operations\",\"datePublished\":\"2026-09-19T21:10:21+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fivemor.com\/?p=7061423\"},\"wordCount\":895,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fivemor.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/fivemor.com\/?p=7061423#primaryimage\"},\"thumbnailUrl\":\"https:\/\/fivemor.com\/wp-content\/uploads\/2026\/09\/LLMOps-vs-MLOps-vs-AgentOps.png\",\"keywords\":[\"evolution\",\"Operations\"],\"articleSection\":[\"Analytics\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fivemor.com\/?p=7061423#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fivemor.com\/?p=7061423\",\"url\":\"https:\/\/fivemor.com\/?p=7061423\",\"name\":\"The Evolution of AI Operations - 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