{"id":538,"date":"2026-10-16T18:31:38","date_gmt":"2026-10-16T13:01:38","guid":{"rendered":"https:\/\/bigstories.net\/blog\/?p=538"},"modified":"2026-10-16T18:31:38","modified_gmt":"2026-10-16T13:01:38","slug":"ai-chatbots-vs-ai-agents","status":"publish","type":"post","link":"https:\/\/bigstories.net\/blog\/ai-chatbots-vs-ai-agents\/","title":{"rendered":"AI Chatbots vs AI Agents: Key Differences Businesses Should Know"},"content":{"rendered":"<p><strong>A chatbot talks to your users. An AI agent gets work done for them.<\/strong> That is the core difference between AI chatbots and AI agents in plain English. Put a chatbot on your website and it will answer questions, fetch a help article, or walk someone through a preset script. Give an AI agent a goal and it works out the steps required, logs into your software via APIs, pulls data, updates records, and finishes the job without waiting to be hand-held at every turn.<\/p>\n<p>Go back eighteen months and nearly every boardroom conversation about conversational AI was really about chatbots. Not anymore. Today, almost every software vendor on the market claims to sell &#8220;AI agents&#8221;. A handful of those products are genuinely new technology. Plenty are just old chatbots with a fresh coat of paint and a bigger price tag. If you sign off on IT budgets or run operations, telling <strong>AI chatbots vs AI agents<\/strong> apart is not pedantry. It dictates what the software can actually do, how much your cloud bill will climb, where your security holes are, and who takes the blame when something breaks.<\/p>\n<p>Buyers are already getting burned. Back in June 2025, Gartner warned that more than 40% of agentic AI projects will be cancelled before the end of 2027. Why? Runaway costs, fuzzy business cases, and weak risk controls. Out of thousands of software suppliers bragging about agentic features, Gartner reckoned only around 130 had built true agents. They called the rest of it &#8220;agent washing&#8221;.<\/p>\n<p>We wrote this guide for CEOs, CTOs, founders, IT heads, and operations leaders in UK and US organisations. Below, we strip away the vendor buzzwords to compare chatbots and agents side by side, look at the real numbers behind Salesforce&#8217;s help-portal rollout, unpack the security traps of autonomous software, and help you pick the right tool for your use case.<\/p>\n<h2>What Is an AI Chatbot?<\/h2>\n<p>An AI chatbot is software built to hold a text or voice conversation with a person so they can get an answer or tick off a simple, routine task. You type a message; the bot replies. Everything happens one turn at a time.<\/p>\n<h3>Rule-Based vs Generative AI Chatbots<\/h3>\n<p>Most business chatbots fall into one of two camps:<\/p>\n<ul>\n<li><strong>Rule-based chatbots:<\/strong> Think of these like interactive phone trees on a screen. They follow strict scripts, button menus, and keyword triggers. You know exactly what they will say, and they cost very little to run. The catch? If a customer types something slightly outside the script, the bot hits a dead end.<\/li>\n<li><strong>Generative AI chatbots:<\/strong> These run on large language models (LLMs), so they understand normal human phrasing and write natural replies. To stop them making things up, smart businesses hook them up to an internal knowledge base using Retrieval-Augmented Generation<strong> (RAG)<\/strong>. That forces the bot to pull its answers straight from your approved company PDFs, wikis, and help articles.<\/li>\n<\/ul>\n<h3>Where AI Chatbots Hit a Wall<\/h3>\n<p>Even when built properly by an experienced team offering <a href=\"https:\/\/www.iihglobal.com\/ai-chatbot-development-services\/\" target=\"_blank\" rel=\"noopener\">AI chatbot development services<\/a>, a chatbot is still built to chat, not to run your back office. Its limits show up quickly:<\/p>\n<ul>\n<li>It cannot string together complex tasks across three or four different software systems on its own.<\/li>\n<li>It has no real sense of planning. Every message is treated as a quick question-and-answer exchange.<\/li>\n<li>The human user has to do the driving. Stop asking questions, and the bot stops working.<\/li>\n<li>As soon as a request needs a judgement call or a multi-system fix, the bot has to pass the ticket to a human colleague.<\/li>\n<\/ul>\n<h2>What Is an AI Agent?<\/h2>\n<p>An AI agent uses a language model as a reasoning engine rather than just a text writer. Instead of waiting to be fed prompts line by line, an agent is given an outcome to achieve. It works out a plan, picks the right digital tools, runs them, checks whether the step worked, and adjusts course if an error pops up.<\/p>\n<p>Because LLM-powered agents can trigger APIs and webhooks, they do far more than search help docs. They query SQL databases, update CRM deals, raise purchase orders, fill out forms, send emails, and kick off workflows in other software.<\/p>\n<h3>Five Things That Make an AI System an &#8220;Agent&#8221;<\/h3>\n<ol>\n<li><strong>Goal focus:<\/strong> It chases a finished outcome, like clearing a billing dispute, rather than just sending a polite reply.<\/li>\n<li><strong>Step-by-step planning:<\/strong> Give it a messy request and it breaks the job down into a logical checklist.<\/li>\n<li><strong>Tool use:<\/strong> It connects to live business software to read data and write changes back.<\/li>\n<li><strong>Working memory:<\/strong> It remembers what happened four steps ago in a long task, and can keep track of context across separate sessions.<\/li>\n<li><strong>Course correction:<\/strong> If an API times out or a database query comes back empty, the agent tries another route instead of crashing.<\/li>\n<\/ol>\n<h3>Customer-Facing vs Back-Office Agents<\/h3>\n<p>Not every AI agent sits inside a website chat widget. Some are customer-facing, like a support agent that checks why an item arrived damaged, verifies the buyer&#8217;s policy tier, issues a refund in Stripe, and books a courier collection from start to finish. Others work quietly in the background with no chat screen at all. An internal agent might match supplier invoices against warehouse receipts at midnight, triage incoming IT tickets by running server diagnostics, or pull together a one-page client briefing from Salesforce notes and email threads before a morning sales call.<\/p>\n<h2>AI Chatbots vs AI Agents: Side-by-Side Comparison<\/h2>\n<p>If you are weighing up quotes from vendors or planning an internal build, here is how an AI chatbot vs AI agent stacks up across ten practical areas.<\/p>\n<table>\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>AI Chatbot<\/th>\n<th>AI Agent<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Main job<\/strong><\/td>\n<td>Answer questions and guide conversations<\/td>\n<td>Finish multi-step tasks and hit a defined goal<\/td>\n<\/tr>\n<tr>\n<td><strong>How it acts<\/strong><\/td>\n<td>Reacts to each message as it arrives<\/td>\n<td>Plans ahead and works through several steps<\/td>\n<\/tr>\n<tr>\n<td><strong>Software access<\/strong><\/td>\n<td>Read-only docs or a single simple lookup<\/td>\n<td>Reads and writes data across multiple systems<\/td>\n<\/tr>\n<tr>\n<td><strong>Job length<\/strong><\/td>\n<td>Single reply or a short scripted flow<\/td>\n<td>Multi-step workflows that can run over time<\/td>\n<\/tr>\n<tr>\n<td><strong>Independence<\/strong><\/td>\n<td>Low<\/td>\n<td>Ranges from human-checked to highly independent<\/td>\n<\/tr>\n<tr>\n<td><strong>Under the hood<\/strong><\/td>\n<td>Decision trees or an LLM + RAG search pipeline<\/td>\n<td>LLM reasoning loop, tool calling, memory, and guardrails<\/td>\n<\/tr>\n<tr>\n<td><strong>Build effort<\/strong><\/td>\n<td>Lower: clean docs, prompt setup, chat UI<\/td>\n<td>Higher: API plumbing, state tracking, testing, monitoring<\/td>\n<\/tr>\n<tr>\n<td><strong>What can go wrong<\/strong><\/td>\n<td>Giving a wrong or unhelpful text answer<\/td>\n<td>Taking the wrong action inside live company software<\/td>\n<\/tr>\n<tr>\n<td><strong>Governance needed<\/strong><\/td>\n<td>Fact-checking, tone rules, basic data privacy<\/td>\n<td>Strict user permissions, sign-off limits, audit logs, kill switches<\/td>\n<\/tr>\n<tr>\n<td><strong>Cost profile<\/strong><\/td>\n<td>Cheaper to build with steady, predictable running costs<\/td>\n<td>Higher build cost and variable LLM token usage per task<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Why Agents Cost More to Build and Run<\/h3>\n<p>From a CTO&#8217;s chair, the cost gap comes down to how many times the system calls the AI model. When a customer asks a RAG chatbot a question, the software runs one search against your documents and makes one call to the language model to write the answer. One question equals one billable call.<\/p>\n<p>An AI agent works in a loop. To sort out a single customer refund, the agent might call the language model to plan the job, call an API to check the order, call the model again to read the result, query the warehouse database, check the refund policy, and call the billing API. One user request can easily trigger ten or fifteen model calls behind the scenes. That extra compute, plus the engineering time needed to test every API connection safely, is why an agent needs a much stronger business case than a chatbot.<\/p>\n<h2>Where Virtual Assistants Fit In<\/h2>\n<p>People often ask where virtual assistants sit in the chatbot vs AI agent debate. Honestly, &#8220;virtual assistant&#8221; is a broad marketing label rather than a technical category. Plenty of older virtual assistants are just dressed-up chatbots. Newer ones built into banking apps or office software can actually move money, book flights, or update spreadsheets, which makes them behave much more like agents. Ignore what the vendor calls the product on the sales deck. Ask what systems it can write to and how many steps it can plan on its own.<\/p>\n<h2>Real Business Example: Salesforce&#8217;s Agentforce on Its Help Site<\/h2>\n<p>To see how this plays out at scale, look at what happened when Salesforce put its own Agentforce agents to work on its main customer support portal, help.salesforce.com.<\/p>\n<h3>The Problem and the Rollout<\/h3>\n<p>Salesforce runs support for a huge global user base. Their old setup relied on self-service search and scripted bots. Those tools handled basic queries fine, but anything tricky still landed on the desks of human support engineers, and customers wanted 24\/7 answers without waiting in a queue.<\/p>\n<p>In October 2024, Salesforce rolled out Agentforce agents across help.salesforce.com, describing it as one of the biggest agentic deployments live at the time. Instead of walking users down fixed decision trees, the agents were built to reason through technical questions, pull answers from company knowledge, and take action, such as logging support cases or routing tough bugs to the right engineering team.<\/p>\n<p>How did they train it? Salesforce treated the agent like a new staff member. First, they fed it roughly 740,000 pieces of help documentation so it had a solid factual base. Then they watched every conversation where a customer asked to speak to a human. By checking what the human engineer did that the agent missed, the team kept coaching and tightening the agent&#8217;s skills. And if a customer genuinely needed a person, they could still reach one quickly.<\/p>\n<h3>The Results and What You Should Steal From Them<\/h3>\n<p>Six months in, Salesforce reported that the agents had handled over 500,000 conversations and were resolving more than 84% of customer questions on their own. Only about 4% of chats were passed to a human engineer. Interestingly, Salesforce pointed out that 4% was a deliberate target, not a failure: some high-stakes problems should always go to a person. By July 2025, the agent had passed the one-million-conversation mark.<\/p>\n<p>Three practical lessons jump out here. First, an agent is useless if your underlying help docs are a mess. Second, you need a feedback loop from human handovers to keep fixing the agent&#8217;s blind spots. Third, never hide the &#8220;talk to a human&#8221; button.<\/p>\n<h2>When to Choose a Chatbot and When to Choose an Agent<\/h2>\n<p>Keep one rule of thumb taped to your monitor: <strong>do not build an AI agent where a simple chatbot or a standard automation script will do the job.<\/strong><\/p>\n<p>Agents only earn their keep when a task needs real judgement across several steps and software tools. If a process follows the exact same rules every Tuesday morning, normal software automation (like Zapier, Make, or RPA scripts) will run faster, cost pennies, and never hallucinate.<\/p>\n<table>\n<thead>\n<tr>\n<th>Business Scenario<\/th>\n<th>Better Fit<\/th>\n<th>Why<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Answering website FAQs, product specs, or HR policy questions<\/td>\n<td><strong>AI Chatbot (RAG)<\/strong><\/td>\n<td>Looking up approved text is enough; no systems need updating.<\/td>\n<\/tr>\n<tr>\n<td>Checking an order delivery status or application progress<\/td>\n<td><strong>Chatbot with one integration<\/strong><\/td>\n<td>Single database lookup with virtually zero operational risk.<\/td>\n<\/tr>\n<tr>\n<td>Sorting out a refund that needs checks across billing and shipping<\/td>\n<td><strong>AI Agent with spending limits<\/strong><\/td>\n<td>Needs multi-step checks across more than one system.<\/td>\n<\/tr>\n<tr>\n<td>Internal IT ticket triage, log checks, and routine fixes<\/td>\n<td><strong>AI Agent<\/strong><\/td>\n<td>Repeatable workflows using clear diagnostic tools.<\/td>\n<\/tr>\n<tr>\n<td>Building weekly management reports from CRM, finance, and email<\/td>\n<td><strong>AI Agent<\/strong><\/td>\n<td>Pulls and combines messy information from several places.<\/td>\n<\/tr>\n<tr>\n<td>High-stakes calls on credit, medical care, or hiring<\/td>\n<td><strong>Human-led with AI support<\/strong><\/td>\n<td>Legal accountability, UK GDPR rules, and human fairness come first.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Risks Unique to Agentic AI (and How to Govern Them)<\/h2>\n<p>When a chatbot goes wrong, someone reads a silly sentence. When an AI agent goes wrong, it can wipe a database row, email the wrong client, or approve a bad refund. Giving AI hands means dealing with five distinct risks:<\/p>\n<ul>\n<li><strong>Overly broad permissions:<\/strong> Give an agent an admin API key and a misunderstood prompt can cause real damage across your records.<\/li>\n<li><strong>Prompt injection:<\/strong> Cleverly hidden instructions tucked inside an incoming email, a web page, or a PDF invoice can trick an agent into ignoring its rules and leaking data.<\/li>\n<li><strong>Snowballing mistakes:<\/strong> If an agent gets a number slightly wrong in step one of a seven-step job, it will confidently build on that mistake for the next six steps.<\/li>\n<li><strong>Surprise cloud bills:<\/strong> An agent stuck in an error loop can fire off hundreds of LLM calls and API requests before anyone notices.<\/li>\n<li><strong>Muddy accountability:<\/strong> Without proper logs, your IT team will have no idea why an agent decided to change a customer&#8217;s account settings at 3 am.<\/li>\n<\/ul>\n<p>None of these risks mean you have to avoid agents. They just mean you need proper engineering brakes:<\/p>\n<ul>\n<li><strong>Least-privilege access:<\/strong> Only give an agent permission to touch the exact API endpoints it needs for that single job.<\/li>\n<li><strong>Human sign-off gates:<\/strong> Force the agent to pause and ask a human manager before it sends money above a set limit or deletes anything.<\/li>\n<li><strong>Hard spending and step caps:<\/strong> Cut the cord automatically if a task takes more than, say, ten steps or costs more than 50p in tokens.<\/li>\n<li><strong>Full audit trails:<\/strong> Log every prompt, every tool call, and every database response so you can replay what happened.<\/li>\n<\/ul>\n<p>For British businesses, the <strong>UK National Cyber Security Centre (NCSC) Guidelines for Secure AI System Development<\/strong> give your technical team a solid blueprint to follow, alongside UK GDPR rules around automated decision-making. Working with a technical partner experienced in <a href=\"https:\/\/www.iihglobal.com\/ai-development-services\/\" target=\"_blank\" rel=\"noopener\">custom AI development and governance<\/a> ensures these guardrails are baked into the code before anything touches live customer data.<\/p>\n<h2>Five Mistakes Businesses Make When Adopting AI Agents<\/h2>\n<p>If you want to stay out of the 40% of cancelled projects Gartner warned about, watch out for these five traps:<\/p>\n<ol>\n<li><strong>Buying the buzzword:<\/strong> Do not take a vendor&#8217;s word for it. Make them run a live demo showing how their agent handles planning, API errors, and messy data on one of your own workflows.<\/li>\n<li><strong>Putting an agent on top of a broken process:<\/strong> If your staff cannot agree on how a refund or onboarding check works today, an AI agent will only make the mess happen faster. Fix the process first.<\/li>\n<li><strong>Skipping the chatbot phase:<\/strong> Prove you can get clean, accurate answers out of your company documents with a read-only chatbot before you hand an AI the keys to edit your systems.<\/li>\n<li><strong>No clear scoreboard:<\/strong> Decide how you will judge success before writing a line of code, whether that is resolution rate, hours saved per week, or cost per ticket.<\/li>\n<li><strong>Taking humans out of the loop too soon:<\/strong> Keep a person checking the agent&#8217;s work until your logs show it can run hundreds of tasks without tripping up.<\/li>\n<\/ol>\n<h2>The Future of AI Agents in Business<\/h2>\n<p>Where is all this heading? Gartner expects at least 15% of day-to-day work decisions to be made autonomously by agentic AI by 2028, up from practically zero in 2024.<\/p>\n<p>Three shifts are making this easier to build in the real world. First, instead of building one giant agent that tries to do everything, developers are using <strong>multi-agent systems<\/strong>, where smaller specialist agents pass tasks to one another like a relay team. Second, open standards like the <strong>Model Context Protocol (MCP)<\/strong> are cutting down the custom code needed to plug agents into everyday business software. Finally, standard customer chatbots are gaining safe, bite-sized action tools one by one, so the boundary between chatbots and agents will keep blurring. The businesses that win will not be the ones that automate everything on day one; they will be the ones that earn trust one workflow at a time.<\/p>\n<h2>Conclusion<\/h2>\n<p>AI chatbots and AI agents are built for different jobs. Chatbots are still the quickest, safest, and most affordable way to answer everyday questions and guide users through your site. AI agents can take multi-step admin off your team&#8217;s plate across several software systems, provided you put the right engineering, security permissions, and human checks around them.<\/p>\n<p>In practice, most growing companies end up using both: starting with a reliable, well-grounded chatbot and bolting on agent capabilities where the time saved clearly justifies the build cost.<\/p>\n<p>IIH Global builds both setups for UK and international clients through our <a href=\"https:\/\/www.iihglobal.com\/ai-chatbot-development-services\/\" target=\"_blank\" rel=\"noopener\">AI chatbot development services<\/a> and bespoke <a href=\"https:\/\/www.iihglobal.com\/generative-ai-services\/\" target=\"_blank\" rel=\"noopener\">generative AI services<\/a>. If you want an honest technical view on which approach fits your workflow, <a href=\"https:\/\/www.iihglobal.com\/get-in-touch\/\" target=\"_blank\" rel=\"noopener\">get in touch with our team<\/a> and let&#8217;s talk through your use case.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the main difference between an AI chatbot and an AI agent?<\/h3>\n<p>A chatbot is built to talk; an AI agent is built to act. Chatbots answer questions and walk users through set conversations one message at a time. An AI agent takes a goal, works out a multi-step plan, logs into your business software via APIs, and finishes tasks with minimal human hand-holding.<\/p>\n<h3>Is ChatGPT a chatbot or an AI agent?<\/h3>\n<p>It started life as a straight chatbot. Today, it sits across both camps. When you use it to draft an email or ask a question, it acts like a chatbot. When it browses the web, runs Python code, or connects to your apps to carry out a multi-step job, it behaves like an AI agent.<\/p>\n<h3>Are AI agents safe for businesses to use?<\/h3>\n<p>They can be very safe, provided you put proper engineering brakes on them. That means giving the agent read\/write access only to the exact tools it needs, forcing human sign-off on high-value actions like big refunds, setting hard spending limits on tokens, and keeping full audit logs of every step it takes.<\/p>\n<h3>What is agentic AI?<\/h3>\n<p>Agentic AI describes software systems that can pursue a goal with a degree of independence. Instead of waiting for a human to prompt every single step, an agentic system breaks a job into tasks, uses external software tools, checks whether its actions worked, and adapts its plan if something changes.<\/p>\n<h3>How much does an AI agent cost compared to a chatbot?<\/h3>\n<p>AI agents cost more both upfront and month to month. Building one takes extra engineering time to wire up APIs, test edge cases, and lock down security permissions. Running costs are also higher because a chatbot usually makes one AI model call per question, whereas an agent might make ten or fifteen calls behind the scenes to finish one task.<\/p>\n<h3>Should my business start with a chatbot or an AI agent?<\/h3>\n<p>Almost every business should start with a well-grounded chatbot first. Building a chatbot that pulls answers from your company documents forces you to clean up your knowledge base and proves the AI can give accurate answers safely. Once you trust its answers, you can give it tools to start taking actions as an agent.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Chatbots talk, agents act. Learn the practical differences between AI chatbots and AI agents, when each fits, the risks of agentic AI and how to choose wisely.<\/p>\n","protected":false},"author":4,"featured_media":539,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":{"subtitle":"","format":"standard","override":[{"template":"2","parallax":"1","layout":"no-sidebar-narrow","sidebar":"default-sidebar","second_sidebar":"default-sidebar","sticky_sidebar":"1","share_position":"topbottom","share_float_style":"share-normal","show_share_counter":"1","show_view_counter":"1","show_featured":"1","show_post_meta":"1","show_post_author":"1","show_post_author_image":"1","show_post_date":"1","post_date_format":"default","post_date_format_custom":"Y\/m\/d","show_post_category":"1","show_post_reading_time":"1","post_reading_time_wpm":"300","post_calculate_word_method":"str_word_count","show_zoom_button":"0","zoom_button_out_step":"2","zoom_button_in_step":"3","show_post_tag":"1","show_popup_post":"1","number_popup_post":"1","show_author_box":"1","show_post_related":"1"}],"image_override":[{"single_post_thumbnail_size":"crop-500","single_post_gallery_size":"crop-500"}],"trending_post_position":"meta","trending_post_label":"Trending","sponsored_post_label":"Sponsored by","disable_ad":"0"},"jnews_primary_category":[],"jnews_social_meta":[],"jnews_override_counter":{"view_counter_number":"0","share_counter_number":"0","like_counter_number":"0","dislike_counter_number":"0"},"footnotes":""},"categories":[92],"tags":[644,645,651,643,647,646,652,655,656,648,654,653,649,650],"class_list":["post-538","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-agentic-ai","tag-ai-agent-capabilities","tag-ai-agent-use-cases","tag-ai-chatbots-vs-ai-agents","tag-autonomous-ai-workflows","tag-chatbot-limitations","tag-chatbot-vs-virtual-assistant","tag-enterprise-ai-agents","tag-future-of-ai-agents","tag-generative-ai-agents","tag-llm-powered-agents","tag-multi-step-ai-agents","tag-rule-based-chatbots","tag-task-oriented-ai-agents"],"_links":{"self":[{"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/posts\/538","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/comments?post=538"}],"version-history":[{"count":1,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/posts\/538\/revisions"}],"predecessor-version":[{"id":540,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/posts\/538\/revisions\/540"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/media\/539"}],"wp:attachment":[{"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/media?parent=538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/categories?post=538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bigstories.net\/blog\/wp-json\/wp\/v2\/tags?post=538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}