{"id":13887,"date":"2026-06-08T17:32:49","date_gmt":"2026-06-08T12:02:49","guid":{"rendered":"https:\/\/www.gmtasoftware.com\/blog\/?p=13887"},"modified":"2026-06-08T17:55:47","modified_gmt":"2026-06-08T12:25:47","slug":"ai-agents-for-customer-service","status":"publish","type":"post","link":"https:\/\/www.gmtasoftware.com\/blog\/ai-agents-for-customer-service\/","title":{"rendered":"AI Agent for Customer Service: Use Cases, Benefits &#038; Implementation"},"content":{"rendered":"<div class=\"blog_summry\">\n<div class=\"blog_summry_box\">\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-13898\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1.webp\" alt=\"ai agents for customer services\" width=\"1920\" height=\"630\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1.webp 1920w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1-300x98.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1-1024x336.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1-768x252.webp 768w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Background-1-1536x504.webp 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/p>\n<p><strong>Key Takeaways<\/strong><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li>The<a href=\"https:\/\/www.ringly.io\/blog\/ai-customer-service-statistics-2026\" rel=\"noopener\"> <span style=\"font-weight: 400;\">global AI customer service market is valued at $15.12 billion in 2026<\/span><\/a><span style=\"font-weight: 400;\">, growing at 25.8% annually<\/span><\/li>\n<li>AI agents have cut cost-per-contact from $4.60 to $1.45, a 68% reduction, across companies surveyed by Freshworks<\/li>\n<li><a href=\"https:\/\/bayelsawatch.com\/ai-customer-service-statistics\/\" rel=\"noopener\">80% of routine customer interactions<\/a> will be handled entirely by AI by 2026<\/li>\n<li><a href=\"https:\/\/www.ringly.io\/blog\/ai-customer-service-statistics-2026\" rel=\"noopener\">Gartner projects $80 billion<\/a> in contact center labor cost savings by the end of 2026<\/li>\n<li>This guide covers what AI agents actually do in customer service, how the architecture works, which features matter in production, and a step-by-step build approach for LLM-based agents<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_Are_AI_Agents_for_Customer_Service\"><\/span><b>What Are AI Agents for Customer Service?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">\u00a0An AI agent for customer service reads a customer query, decides what action to take, connects to your business systems, and resolves the issue \u2014 without a human involved at any step. A rule-based chatbot follows a script. An AI agent decides. Understanding the <a href=\"https:\/\/www.gmtasoftware.com\/blog\/types-of-ai-agents\/\"><strong>different types of AI agents<\/strong><\/a> \u2014 from simple reflex systems to goal-based and learning agents \u2014 helps clarify why this distinction matters in production<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The difference is in how they are built.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Traditional bots run on if-then logic. You write the decision tree, and the bot follows it. AI agents use large language models (LLMs) to understand what a customer wants, connect to other tools, and take action in multiple steps.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u00a0Ask a rule-based bot to process a refund, and it sends you to a form. Ask an AI agent the same thing, and it checks your order history, confirms your return is eligible, starts the refund, and sends a confirmation, with no human involved.<\/span><\/p>\n<p>If you are still deciding which one your business actually needs, this breakdown of <a title=\"ai agent vs ai chatbot\" href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-agent-vs-ai-chatbot\/\"><strong>AI agent vs AI chatbot<\/strong><\/a> covers the full comparison with costs and industry examples.<\/p>\n<p><span style=\"font-weight: 400;\">Three things define a real AI agent:<\/span><\/p>\n<p><b>Autonomy<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It acts toward a goal without being told what to do at each step.<\/span><\/p>\n<p><b>Tool use<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It connects to your systems, your CRM, your order platform, and your ticketing tool to take real action rather than just provide information.<\/span><\/p>\n<p><b>Memory<\/b><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It keeps track of the conversation so customers do not have to repeat themselves.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In 2025, AI agents handled 95% of customer interactions managed by AI. By 2026,<\/span><a href=\"https:\/\/bayelsawatch.com\/ai-customer-service-statistics\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">80% of routine interactions will be fully automated<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p>Agentic AI moving from pilot to production is one of the defining business shifts of 2026 \u2014<\/p>\n<p><a href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-trends\/\">here is how it fits into the broader AI trend landscape this year.<\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Do_AI_Agents_Work_for_Customer_Service\"><\/span><b>How Do AI Agents Work for Customer Service?\u00a0<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">An AI agent runs a repeating loop: read the situation, decide what to do, act, then check the result. Here is what each step looks like on a real support ticket. The entire process of an AI agent working can be understood through the following 6 points &#8211;\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"1_Perception_and_Intent_Classification\"><\/span><b>1. Perception and Intent Classification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent reads the customer message and works out what they need. Not by spotting keywords, but by understanding meaning. An LLM-based agent knows that &#8220;I never got my package&#8221; and &#8220;where is my order&#8221; are the same request, even though the words are different. It also reads tone and urgency from prior messages in the thread.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"2_Context_Retrieval_via_RAG\"><\/span><b>2. Context Retrieval via RAG<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Before responding, the agent searches its knowledge base using Retrieval-Augmented Generation (RAG). RAG connects the LLM to your actual company data: product docs, return policies, pricing, known issues, all in real time. Without RAG, an LLM pulls from general training data, which has none of your company&#8217;s specifics. That is where hallucinations come from.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"3_Reasoning_and_Planning\"><\/span><b>3. Reasoning and Planning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent breaks the customer&#8217;s goal into steps. For a refund request, that might be: confirm the purchase exists, check the return window, look at item condition from previous messages, calculate the refund amount, trigger the refund API. Frameworks like LangGraph manage this process, letting the agent retry a failed step or adjust if a tool returns something unexpected.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"4_Tool_Calls_and_Action_Execution\"><\/span><b>4. Tool Calls and Action Execution<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent calls your integrated systems, CRM, order management, ticketing, and payment processor to get things done. This is what separates an AI agent from a smart FAQ. The agent does not describe what should happen. It makes it happen.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"5_Response_Generation_and_Escalation\"><\/span><b>5. Response Generation and Escalation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent writes a reply in plain language. If the task is outside its authority, a fraud dispute above a set amount, for example, it hands off to a human agent with the full context already in the ticket.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"6_Memory_and_Learning\"><\/span><b>6. Memory and Learning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Within a session, the agent tracks what was said. More advanced setups carry memory across sessions, so a returning customer does not have to re-explain their history every time.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_Off-the-Shelf_AI_Agents_Hit_Their_Ceiling\"><\/span><b>Where Off-the-Shelf AI Agents Hit Their Ceiling<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Platform AI tools have made it easy to add automation to a support operation. Zendesk reached $200 million in AI-related annual recurring revenue in 2025, with nearly 20,000 customers using its AI tools. Across the industry,<\/span><a href=\"https:\/\/www.groovehq.com\/blog\/55-ai-customer-support-statistics\" rel=\"noopener\"> <span style=\"font-weight: 400;\">76% of contact center leaders have adopted human-in-the-loop AI models<\/span><\/a><span style=\"font-weight: 400;\"> as standard practice. Adoption is real and growing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So is the ceiling.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Off-the-shelf platforms are built for the average use case. They work well when your workflows fit neatly inside the platform&#8217;s structure, their native integrations cover your tech stack, and your compliance needs do not go beyond what their standard data handling allows. For many companies, that is fine.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For companies with proprietary backend systems, complex workflows, regulated data, or support operations spanning multiple business units, the platform&#8217;s defaults become limits. You end up bending your processes to fit the tool.<\/span><\/p>\n<p>For small businesses evaluating AI customer service tools for the first time, a platform deployment is often the right entry point.<\/p>\n<p><a href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-for-small-businesses-in-usa\/\"><strong>This guide covers AI tools and setup costs specifically for small businesses in the USA.<\/strong><\/a><\/p>\n<p><span style=\"font-weight: 400;\">The gaps that come up most often:<\/span><\/p>\n<p><b>Integration depth-<\/b><span style=\"font-weight: 400;\"> Platforms connect to a fixed list of third-party tools. If your order management system, internal CRM, or custom backend is not on that list, the agent can read a conversation but cannot act in your systems. That makes it an expensive FAQ bot.<\/span><\/p>\n<p><b>Workflow customisation-<\/b><span style=\"font-weight: 400;\"> Platform agents handle standard ticket flows well. Anything involving multi-step business logic, conditional refund rules, tiered escalation, and cross-system data lookups needs workarounds that add fragility over time.<\/span><\/p>\n<p><b>Compliance and data control-<\/b><span style=\"font-weight: 400;\"> Healthcare, fintech, and other regulated businesses need controls beyond what a SaaS platform&#8217;s standard settings provide. Data residency, granular audit trails, and role-based access at the field level, these usually require custom architecture.<\/span><\/p>\n<p><b>Platform dependency- when<\/b><span style=\"font-weight: 400;\"> your AI agent lives inside a vendor&#8217;s platform, your roadmap depends on theirs. Pricing changes, feature removals, and integration decisions are outside your control.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Businesses that outgrow a platform&#8217;s defaults are not choosing between Zendesk and building from scratch. They are choosing between accepting those limits and working with an <a title=\"AI AGENT DEVELOPMENT\" href=\"https:\/\/www.gmtasoftware.com\/services\/ai-agent-development-company\"><strong>AI agent development company<\/strong><\/a> to build something that fits how they actually operate.<\/span><\/p>\n<p><a href=\"https:\/\/www.gmtasoftware.com\/contact-us\"><img decoding=\"async\" class=\"alignnone size-full wp-image-13893\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Your-AI-Agent-Should-Fit-Your-Business-\u2014Not-the-Other-Way-Around.webp\" alt=\"build ai agent with team of expert \" width=\"1050\" height=\"300\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Your-AI-Agent-Should-Fit-Your-Business-\u2014Not-the-Other-Way-Around.webp 1050w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Your-AI-Agent-Should-Fit-Your-Business-\u2014Not-the-Other-Way-Around-300x86.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Your-AI-Agent-Should-Fit-Your-Business-\u2014Not-the-Other-Way-Around-1024x293.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Your-AI-Agent-Should-Fit-Your-Business-\u2014Not-the-Other-Way-Around-768x219.webp 768w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><\/a><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Key_Advantages_of_AI_Agents_for_Customer_Service\"><\/span><b>Key Advantages of AI Agents for Customer Service<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">When we talk about the adoption of AI agents and how customer service is getting better with the implementation, a strong reason behind it is the advantages that it brings along. Companies today are moving towards AI agents because they offer solutions to a lot of their concerns.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Some of the key advantages of AI agents for customer service include &#8211;\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"247_Availability_Without_Linear_Cost_Growth\"><\/span><b>24\/7 Availability Without Linear Cost Growth<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Human agents cost the same whether they handle 10 tickets or 1,000. AI agents absorb volume spikes, peak sale events, product outages, a complaint going viral without queues or overtime. One deployment can handle thousands of concurrent conversations.<\/span><\/p>\n<p>Customer service is consistently ranked among the highest-ROI applications of enterprise AI \u2014 alongside fraud detection, predictive maintenance, and supply chain forecasting.<\/p>\n<p><a href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-for-enterprise\/\"><strong>See how enterprise AI deployments compare across industries.<\/strong><\/a><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Measurable_Cost_Reduction\"><\/span><b>Measurable Cost Reduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The cost data is consistent. Freshworks reports a 68% drop after AI implementation, from $4.60 to $1.45 per contact. AI self-service contacts cost an average of $1.84 versus $13.50 for human-handled ones. At volume, that gap compounds fast.<\/span><a href=\"https:\/\/www.ringly.io\/blog\/ai-customer-service-statistics-2026\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Gartner puts industry-wide contact center labor savings at $80 billion by the end of 2026<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Faster_Resolution_at_Any_Volume\"><\/span><b>Faster Resolution at Any Volume<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI agents respond in seconds. They do not transfer calls, put customers on hold, or need supervisor approval for standard requests.<\/span><a href=\"https:\/\/bayelsawatch.com\/ai-in-customer-support-statistics\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">61% of customers prefer self-service<\/span><\/a><span style=\"font-weight: 400;\">. Speed without friction is what they expect.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Consistent_Quality\"><\/span><b>Consistent Quality<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A human agent has good days and bad days. An AI agent runs the same reasoning process on every ticket. Tone, accuracy, and policy adherence stay the same at 10 tickets or 10,000.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Human_Agent_Productivity_on_Complex_Work\"><\/span><b>Human Agent Productivity on Complex Work<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI agents do not just replace humans on simple tickets. They make human agents faster at complex ones.<\/span><a href=\"https:\/\/bayelsawatch.com\/ai-customer-service-statistics\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">AI saves human agents roughly one hour per day<\/span><\/a><span style=\"font-weight: 400;\"> by handling lookups, drafting responses, and pre-filling case context. Across a team of 50, that is 50 recovered hours every day.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Personalisation_at_Scale\"><\/span><b>Personalisation at Scale<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI agents pull customer history, past purchases, open tickets, and account status before responding. A human agent has to gather that manually. An AI agent has it ready at the start of every conversation.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Multilingual_Support_Without_Extra_Headcount\"><\/span><b>Multilingual Support Without Extra Headcount<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI agents run across dozens of languages at the same time. Building a human multilingual support team means recruiting, training, and scheduling across time zones. An AI agent handles the same conversation in English, Spanish, or Mandarin from one system.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">With all these added benefits, all you need to identify are the core features that can help you, and your <a href=\"https:\/\/www.gmtasoftware.com\/services\/ai-chatbot-development-company\"><strong>AI chatbot<\/strong><\/a> can definitely make a difference. In case you are wondering what the features are, check out the next section to learn more.\u00a0<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Must-Have_Features_of_AI_Agents_for_Customer_Service\"><\/span><b>Must-Have Features of AI Agents for Customer Service<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-13892\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-9.webp\" alt=\"ai agents for customer services features\" width=\"1200\" height=\"630\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-9.webp 1200w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-9-300x158.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-9-1024x538.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-9-768x403.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">The difference between an agent that resolves 80% of tickets and one that frustrates customers and escalates everything comes down to a few specific features.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The features include &#8211;\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Natural_Language_Understanding_with_Context_Awareness\"><\/span><b>Natural Language Understanding with Context Awareness<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent has to handle vague, misspelled, emotional, and multi-part messages. Customers do not write support tickets like engineers write documentation. An agent that needs clean phrasing will fail on real traffic.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"RAG-Powered_Knowledge_Base_Integration\"><\/span><b>RAG-Powered Knowledge Base Integration<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Static FAQ responses go stale fast. A RAG-connected agent pulls from live documentation, updated policies, and current product data. This is the most important technical feature for keeping wrong answers out of production.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Multi-System_Integration_via_APIs\"><\/span><b>Multi-System Integration via APIs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent is only as useful as the systems it can reach. CRM, ticketing, order management, payments, inventory. It needs API access to take real actions. Read-only connections produce information bots, not AI agents.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Escalation_Logic_with_Full_Context_Handoff\"><\/span><b>Escalation Logic with Full Context Handoff<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The agent has to know what it cannot handle and hand off cleanly. Customers should never have to repeat their story to a human agent. The handoff needs the full conversation history, the identified intent, what was tried, and the relevant account data.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Confidence_Scoring\"><\/span><b>Confidence Scoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Production agents need to measure their own certainty. When confidence drops below a threshold because the query is unusual, emotionally charged, or outside its knowledge, it should escalate rather than guess. This is what stops bad answers from reaching customers.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Sentiment_Detection_and_Tone_Adjustment\"><\/span><b>Sentiment Detection and Tone Adjustment<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An agent who responds differently to a frustrated customer than to a calm one will escalate fewer conversations. Sentiment detection flags at-risk interactions for human review before a customer walks.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Compliance_and_Data_Security\"><\/span><b>Compliance and Data Security<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">In healthcare, fintech, or any regulated sector, the agent needs role-based access control, audit logging, data residency options, and alignment with GDPR, HIPAA, or other applicable rules. These controls need to be built in from the start, not added later.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Omnichannel_Deployment\"><\/span><b>Omnichannel Deployment<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Customers contact support through chat, email, voice, and social media. The agent needs consistent behaviour across all of them, with conversation context intact when a customer switches channels.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Analytics_and_Performance_Monitoring\"><\/span><b>Analytics and Performance Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Resolution rate, escalation rate, CSAT, and average handle time. You need to see all of it. An agent you cannot measure is one you cannot improve.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These features make the entire solution trustworthy while ensuring that the objectives of a bot and autonomy are met successfully.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Steps_to_Build_LLM-Based_AI_Agents_for_Customer_Service\"><\/span><b>Steps to Build LLM-Based AI Agents for Customer Service<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"alignnone wp-image-13894 size-full\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-10.webp\" alt=\"ai agent for customer service process\" width=\"1200\" height=\"630\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-10.webp 1200w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-10-300x158.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-10-1024x538.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Feature-complexity-1920-x-630-px-10-768x403.webp 768w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">While these features and benefits do seem tempting to get started with your own customer service AI agent, it is recommended that you get ready before implementation. The solution is definitely new, and it also requires your existing framework to be ready.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There are some steps that must be carried out to ensure proper implementation of an AI agent for customer service in your existing workflow. These are &#8211;\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_1_Audit_Your_Current_Support_Operations\"><\/span><b>Step 1: Audit Your Current Support Operations<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Before touching any LLM or framework, map your existing support. Pull ticket volume by category, resolution time by type, escalation rates, and your 20 most common queries. This tells you where an AI agent will help and where it will likely struggle. Tickets that need judgment calls, emotional handling, or access to systems with no APIs should stay with humans in the first phase.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_2_Define_Scope_and_Success_Metrics\"><\/span><b>Step 2: Define Scope and Success Metrics<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Start narrow. One ticket category, order status queries, password resets, and billing questions give you a controlled first deployment. Define what success looks like before you build: target resolution rate, escalation threshold, CSAT floor. Agents launched without clear targets get measured on the wrong things.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_3_Choose_Your_LLM_and_Architecture\"><\/span><b>Step 3: Choose Your LLM and Architecture<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Model choice depends on your ticket complexity. GPT-4o and Claude handle nuanced multi-turn conversations well. Smaller fine-tuned models cost less and respond faster to high-volume, simple queries. Most production systems use a tiered approach: a lighter model handles classification and easy resolution, and a larger model takes over on complex or sensitive cases.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For orchestration,<\/span><a href=\"https:\/\/www.turing.com\/resources\/what-are-llm-agents-and-how-to-implement\" rel=\"noopener\"> <span style=\"font-weight: 400;\">LangGraph is the current standard<\/span><\/a><span style=\"font-weight: 400;\"> for multi-step agent workflows with state management and error recovery. LangChain suits simpler pipelines.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_4_Build_and_Ground_Your_Knowledge_Base\"><\/span><b>Step 4: Build and Ground Your Knowledge Base<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Your agent&#8217;s accuracy is limited by the quality of its knowledge base. Compile product documentation, policy documents, FAQ content, and resolved historical tickets. Connect the agent to this via RAG at inference time. Set up a process for keeping it current. Stale information produces confident wrong answers.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_5_Integrate_Your_Business_Systems\"><\/span><b>Step 5: Integrate Your Business Systems<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Map every API connection the agent needs: CRM for customer history, order management for purchase data, ticketing for case creation, payment processor for refunds. Test each one in a sandbox before going live. Integration failures mid-conversation are one of the most damaging failure modes for customers and for internal confidence in the system.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_6_Design_Escalation_Paths\"><\/span><b>Step 6: Design Escalation Paths<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Define exactly which ticket types, confidence thresholds, and customer signals trigger escalation. Build the handoff so the human agent gets full context, the conversation transcript, the identified intent, account data, wand hat was tried, without asking the customer to re-explain. Most deployments underinvest here. Customer frustration builds up right at this point.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_7_Run_Pilots_and_Red-Team_Testing\"><\/span><b>Step 7: Run Pilots and Red-Team Testing<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Before launch, run the agent against real historical tickets. Find where it fails, where it gives wrong information, and where its confidence scoring breaks down. Try to make it produce policy violations, bypass security controls, or hallucinate. In regulated industries, compliance teams need to be in this phase, not brought in after launch.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Step_8_Deploy_with_Monitoring\"><\/span><b>Step 8: Deploy with Monitoring<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Roll out to a controlled slice of traffic first, a percentage of volume or one channel. Monitor resolution rate, escalation rate, CSAT, and error logs in real time. Build a feedback loop so production edge cases improve the knowledge base and escalation logic over time. The first deployment is a starting point.<\/span><\/p>\n<h3><a href=\"https:\/\/www.gmtasoftware.com\/contact-us\"><img decoding=\"async\" class=\"alignnone size-full wp-image-13896\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Thinking-About-Building-an-AI-Agent_.webp\" alt=\"build ai agent with team of expert \" width=\"1050\" height=\"300\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Thinking-About-Building-an-AI-Agent_.webp 1050w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Thinking-About-Building-an-AI-Agent_-300x86.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Thinking-About-Building-an-AI-Agent_-1024x293.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Thinking-About-Building-an-AI-Agent_-768x219.webp 768w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><\/a><\/h3>\n<h2><span class=\"ez-toc-section\" id=\"Customer_Service_AI_Agents_Major_Challenges_and_Their_Solutions\"><\/span><b>Customer Service AI Agents: Major Challenges and Their Solutions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Irrespective of how easy the implementation seems with the experts, even they have some challenges that must be eradicated. These challenges include &#8211;\u00a0<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Hallucination_and_Incorrect_Answers\"><\/span><b>Challenge: Hallucination and Incorrect Answers<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">LLMs produce confident-sounding text whether or not it is accurate. An agent that quotes the wrong return policy or invents a discount code damages trust faster than a slow human response would.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Connect the agent to verified knowledge sources via RAG and configure it to say when it does not have a reliable answer, not guess. Confidence scoring catches the cases RAG does not fully cover.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Low_Customer_Trust_in_AI\"><\/span><b>Challenge: Low Customer Trust in AI<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/neomanex.com\/posts\/ai-customer-service-statistics\" rel=\"noopener\"><span style=\"font-weight: 400;\">72% of customers trust companies less than they did a year ago<\/span><\/a><span style=\"font-weight: 400;\">, per Salesforce data from 2026.<\/span><a href=\"https:\/\/www.groovehq.com\/blog\/55-ai-customer-support-statistics\" rel=\"noopener\"> <span style=\"font-weight: 400;\">46% say AI-powered service rarely leads to a successful outcome<\/span><\/a><span style=\"font-weight: 400;\">. Trust drops fast when AI fails in visible ways.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Tell customers they are talking to an AI. Pair that transparency with genuine resolution capability and make it easy to reach a human. Customers accept AI when it works. When it masks a bad experience with a friendly tone, they do not come back.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Complex_Multi-System_Integrations\"><\/span><b>Challenge: Complex Multi-System Integrations<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An agent that holds a conversation but cannot process a refund or update an account is a sophisticated FAQ. Real actions require API integration with legacy systems that were not built with AI in mind.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Treat integration as the primary timeline driver, not a detail. Audit your system landscape for API availability before committing to agent capabilities. For systems with no API access, plan a data integration layer first.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Handling_Emotionally_Charged_Interactions\"><\/span><b>Challenge: Handling Emotionally Charged Interactions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Upset customers tend to get more frustrated with AI, particularly when it feels unresponsive. Agents without sentiment detection route distressed customers through automated flows when they need a real person.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Add sentiment detection with automatic escalation for high-distress signals. Some interactions should never be handled autonomously. A customer disputing a fraudulent charge or reporting a serious product failure belongs with a human.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Data_Privacy_and_Compliance\"><\/span><b>Challenge: Data Privacy and Compliance<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Customer service conversations carry personal data, financial information, and sometimes protected health data. An AI agent that mishandles this creates regulatory exposure.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Build compliance controls into the architecture from the start. Role-based access limits what the agent retrieves. Audit logging tracks every action. Data residency controls keep conversation data in approved regions. For fintech and healthcare, get legal and compliance teams involved before architecture decisions are made.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Challenge_Maintaining_Performance_at_Scale\"><\/span><b>Challenge: Maintaining Performance at Scale<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An agent running well at 1,000 tickets a day can degrade at 100,000. Knowledge base retrieval slows down, API latency builds up, and edge cases multiply.<\/span><\/p>\n<p><b>Solution:<\/b><span style=\"font-weight: 400;\"> Design for load from day one. Cache common retrieval queries, use async processing for non-urgent actions, and run load tests before peak periods. Plan the LLM cost model at scale. Inference costs that look fine in a pilot can become significant in production.<\/span><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_GMTA_Software_Is_Transforming_the_Future_of_Customer_Service_with_AI_Agents\"><\/span><b>How GMTA Software Is Transforming the Future of Customer Service with AI Agents<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Our experts at GMTA Software <a title=\"AI Agent development guide\" href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-agent-development-guide\/\"><strong>build custom AI agents<\/strong><\/a> for customer service that are built for production from day one, not sandbox demos that break under real load.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our <a title=\"ai agent development company\" href=\"https:\/\/www.gmtasoftware.com\/services\/ai-agent-development-company\"><strong>AI agent development services<\/strong><\/a> cover the full lifecycle: architecture, LLM selection, RAG knowledge base setup, multi-system integration, compliance review, and post-deployment monitoring. We have deployed agentic AI across retail, fintech, SaaS, and healthcare clients, from first-contact resolution systems to intelligent escalation routing that cuts human agent workload by an average of 40%.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The most common reason deployments fail is that the agent cannot take real actions in your systems. It can only describe what should happen. Our team builds the API connections, data pipelines, and workflow logic that let your AI agent process refunds, update accounts, create tickets, and surface the right information without a human in the loop.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We also build for compliance. Our AI agents for regulated industries include role-based access control, audit logging, and data handling frameworks aligned to GDPR, <a title=\"HIPAA COMPLIANT HEALTHCARE APP DEVELOPMENT\" href=\"https:\/\/www.gmtasoftware.com\/blog\/hipaa-compliant-app-development\/\"><strong>HIPAA<\/strong><\/a>, and SOC 2 requirements.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you are evaluating AI agent development services and want to understand what a realistic deployment timeline, cost structure, and capability scope look like for your business, contact GMTA Software to start the conversation.<\/span><\/p>\n<h3><a href=\"https:\/\/www.gmtasoftware.com\/contact-us\"><img decoding=\"async\" class=\"alignnone size-full wp-image-13895\" src=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Frame-1948761477.webp\" alt=\"build ai agent with team of expert \" width=\"1050\" height=\"300\" srcset=\"https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Frame-1948761477.webp 1050w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Frame-1948761477-300x86.webp 300w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Frame-1948761477-1024x293.webp 1024w, https:\/\/www.gmtasoftware.com\/blog\/wp-content\/uploads\/2026\/06\/Frame-1948761477-768x219.webp 768w\" sizes=\"(max-width: 1050px) 100vw, 1050px\" \/><\/a><\/h3>\n<h2><span class=\"ez-toc-section\" id=\"FAQs\"><\/span><b>FAQs<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3><span class=\"ez-toc-section\" id=\"What_is_an_AI_agent_for_customer_service\"><\/span><b>What is an AI agent for customer service?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">An AI agent for customer service uses large language models and connected business systems to understand customer queries, work out the right response, and take real actions, processing refunds, updating accounts, creating tickets, without a human directing each step. It differs from a chatbot in its ability to act across multiple steps without being told what to do at each one.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_are_the_top_AI_agents_for_customer_service_in_2026\"><\/span><b>What are the top AI agents for customer service in 2026?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The platforms with the strongest production results in 2026 include<\/span><a href=\"https:\/\/www.sobot.io\/blog\/ai-agent-tools-customer-service-platforms-2026\/\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Intercom Fin (67% average autonomous resolution across 7,000+ customers)<\/span><\/a><span style=\"font-weight: 400;\">, Zendesk AI agents, Sierra (built for Fortune 1000 enterprise deployments), and Decagon. Choose based on your integration requirements, ticket complexity, and compliance needs, not vendor marketing figures.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_much_do_AI_agent_development_services_cost\"><\/span><b>How much do AI agent development services cost?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\"><a title=\"AI Agent development cost\" href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-agent-development-cost\/\"><strong>AI agents&#8217; development cost<\/strong> <\/a>depends on the scope. A deployment handling one or two ticket categories on an existing platform like Zendesk or Intercom typically costs $20,000 to $80,000. A custom-built agent with deep multi-system integration, RAG knowledge infrastructure, and compliance controls for a regulated industry ranges from $150,000 to $500,000 or more. Ongoing costs include LLM inference, knowledge base upkeep, and monitoring.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_long_does_it_take_to_deploy_an_AI_agent_for_customer_service\"><\/span><b>How long does it take to deploy an AI agent for customer service?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Platform-based deployments with limited integration scope take 4 to 10 weeks. Custom LLM-based agents with complex integrations and compliance requirements take 3 to 6 months from proof of concept to production. Integration with legacy systems is usually what drives the timeline longer.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_is_agentic_AI_for_customer_service\"><\/span><b>What is agentic AI for customer service?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Agentic AI for customer service refers to AI systems that work toward customer service goals on their own: reading queries, planning multi-step resolutions, using tools in connected systems, and learning from outcomes. The word &#8220;agentic&#8221; means the system acts toward a goal rather than following a fixed script.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"How_do_AI_agents_handle_escalation_to_human_agents\"><\/span><b>How do AI agents handle escalation to human agents?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI agents with escalation logic watch confidence scores, sentiment signals, and ticket complexity. When a trigger is met, the agent transfers the conversation to a human agent with full context: the conversation history, the identified problem, what was tried, and relevant account data. The customer should not have to explain the situation again.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"Can_AI_agents_for_customer_service_handle_regulated_industries\"><\/span><b>Can AI agents for customer service handle regulated industries?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Yes, with the right build. Deployments in <a href=\"https:\/\/www.gmtasoftware.com\/blog\/ai-in-healthcare\/\"><strong>healthcare<\/strong><\/a>, fintech, and other regulated sectors need role-based access control, audit logging, data residency options, and alignment with HIPAA, GDPR, or applicable rules. These controls need to be designed into the system from the start.<\/span><\/p>\n<h3><span class=\"ez-toc-section\" id=\"What_is_the_typical_resolution_rate_for_customer_service_AI_agents\"><\/span><b>What is the typical resolution rate for customer service AI agents?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Resolution rates vary by ticket type and integration depth.<\/span><a href=\"https:\/\/www.ringly.io\/blog\/ai-customer-service-statistics-2026\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Ecommerce brands using autonomous AI agents report 76% to 92% resolution rates on standard ticket types<\/span><\/a><span style=\"font-weight: 400;\">. Customer-perceived resolution, where the customer actually considers the issue closed, typically runs lower than what systems measure as deflection. Realistic targets for a first deployment are 40% to 60% autonomous resolution, improving as the knowledge base and escalation logic develop.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways The global AI customer service market is valued at $15.12 billion in 2026, growing at 25.8% annually AI agents have cut cost-per-contact from $4.60 to $1.45, a 68% reduction, across companies surveyed by Freshworks 80% of routine customer interactions will be handled entirely by AI by 2026 Gartner projects $80 billion in contact [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":13891,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1549],"tags":[],"class_list":["post-13887","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agent-development"],"acf":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/posts\/13887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/comments?post=13887"}],"version-history":[{"count":5,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/posts\/13887\/revisions"}],"predecessor-version":[{"id":13899,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/posts\/13887\/revisions\/13899"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/media\/13891"}],"wp:attachment":[{"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/media?parent=13887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/categories?post=13887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.gmtasoftware.com\/blog\/wp-json\/wp\/v2\/tags?post=13887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}