{"id":217026,"date":"2026-09-02T06:16:32","date_gmt":"2026-09-02T06:16:32","guid":{"rendered":"https:\/\/10pearls.com\/uk\/blog\/\/"},"modified":"2026-09-02T06:16:36","modified_gmt":"2026-09-02T06:16:36","slug":"agentic-ai-implementation","status":"publish","type":"post","link":"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-implementation\/","title":{"rendered":"Agentic AI Implementation: How to Build AI Agents"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"217026\" class=\"elementor elementor-217026\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7fa80a8 e-flex e-con-boxed e-con e-parent\" data-id=\"7fa80a8\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d3e0b3f elementor-widget__width-initial indigo-h1 elementor-widget elementor-widget-heading\" data-id=\"d3e0b3f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">What is Agentic AI and Why\nit Matters for Enterprises<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-93868c9 elementor-icon-list--layout-inline elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"93868c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items elementor-inline-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">10Pearls Editorial Team<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item elementor-inline-item\">\n\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-circle\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M256 8C119 8 8 119 8 256s111 248 248 248 248-111 248-248S393 8 256 8z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">24 min read<\/span>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7619296 e-flex e-con-boxed e-con e-parent\" data-id=\"7619296\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-b3327b0 e-con-full e-flex e-con e-child\" data-id=\"b3327b0\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ede6120 elementor-widget elementor-widget-heading\" data-id=\"ede6120\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Summary<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b907a3b elementor-widget elementor-widget-text-editor\" data-id=\"b907a3b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>This guide covers the agentic AI implementation journey, from use case selection and architecture to development, testing, production deployment, observability, and governance, helping enterprises build reliable agents that deliver measurable value while managing cost, risk, and performance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d33b606 e-con-full e-flex e-con e-child\" data-id=\"d33b606\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-3f18313 e-con-full e-flex e-con e-child\" data-id=\"3f18313\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1f07c0a section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"1f07c0a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Agentic AI is the hottest thing in enterprise technology right now, and for good reason. But there&#8217;s a gap that doesn&#8217;t get talked about enough: the vast majority of agent projects never make it past the demo stage.<\/p><p>This guide walks through the full arc of agentic AI implementation, from picking the right use case through architecture, build, testing, and production. In one line: <a href=\"\/blog\/what-is-agentic-ai\/\" target=\"_blank\" rel=\"noopener\">agentic AI<\/a> refers to autonomous, goal-driven systems that can plan, reason, and take actions using tools, well beyond what a chatbot does when it answers a single question.<\/p><p>Getting from a working prototype to a system running reliably in production is where most of the real work, and most of the failure, actually happens.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-78266f0 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"78266f0\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"whatAgentic\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What Agentic AI Implementation Actually Involves<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3737389 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"3737389\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><a href=\"\/artificial-intelligence\/generative-ai-development-services\/\" target=\"_blank\" rel=\"noopener\">Generative AI<\/a> is widely used to generate content in response to a prompt. Agentic AI goes a step further: It creates a sequence of actions to adhere to, utilizes resources to complete tasks, observes the ongoing developments during a project, and is capable of functioning autonomously without requiring repeated guidance. That&#8217;s the core distinction between <br \/>agentic AI and generative AI, and it matters because agents introduce failure modes chatbots simply don&#8217;t have.<\/p><p>The core capabilities that make a system agentic are:<\/p><ul><li>Planning (breaking a goal into steps)<\/li><li>Tool use (calling APIs, databases, or other systems to act)<\/li><li>Memory (retaining context across a multi-step task)<\/li><li>Autonomy (executing without a human approving every single action).<\/li><\/ul><p>Together, these form the agentic AI workflow that distinguishes an agent from a simple <br \/>prompt-response tool.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-487023a section-head-margin-bottom elementor-widget elementor-widget-image\" data-id=\"487023a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"860\" height=\"454\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body1.webp\" class=\"attachment-large size-large wp-image-217391\" alt=\"\" srcset=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body1.webp 860w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body1-300x158.webp 300w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body1-768x405.webp 768w\" sizes=\"auto, (max-width: 860px) 100vw, 860px\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-53812b3 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"53812b3\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"useCase\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 1: Choosing the Right Use Case<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4b1897 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"e4b1897\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>This is where most agentic AI implementations either set themselves up for success or doom themselves before a single line of code gets written.<br \/><br \/>A good first agentic use case has a few defining traits.<\/p><ul><li>It has bounded scope: rather than being an open-ended \u201chandle anything the customer asks.\u201d<\/li><li>The use case has clear value: someone in the business can articulate, in a sentence, why this task being automated matters.<\/li><li>It&#8217;s measurable: You need to clearly explain what success means in measurable terms, like how many problems are solved, how much time is saved, or how many mistakes are reduced, instead of just having a general feeling that things seem quicker.<\/li><li>It handles autonomy: the task doesn&#8217;t require perfect judgment on the first try, and the cost of an occasional mistake is manageable.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30f470f indigo-h4 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"30f470f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Where Enterprise gets it wrong<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-92103fa section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"92103fa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tEnterprise teams often see early success in one department and rush to scale AI across the organization. Instead, evaluate each potential use case based on <a href=\"\/blog\/a-guide-to-ai-readiness-assessment-frameworks\/\" target=\"_blank\" rel=\"noopener\">business value, feasibility,\nand risk.<\/a> Prioritize initiatives that deliver high value with low risk and are relatively easy to implement. High-value but high-risk use cases, such as financial transactions or customer-facing workflows should come later. The goal isn&#8217;t to launch the most ambitious AI initiative first, but to choose one that helps your team learn, iterate, and build confidence before tackling more complex deployments.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e19b660 indigo-h3 section-title-margin-bottom elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"e19b660\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"designAgent\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 2: Designing the Agent \u2014 Architecture and Patterns<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2050c9 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"c2050c9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Once you&#8217;ve picked a use case, the next question is what the agent is actually built from. This is where agentic AI architecture becomes the central design problem.<\/p><p>At a component level, most agent architectures share the same building blocks, that is an LLM or reasoning engine that drives decision-making that has memory for information that persists across sessions, tools or function calling that let the agent interact with external systems, a planning module that breaks goals into executable steps, and an <a href=\"\/blog\/building-enterprise-ai-agent-frameworks\/\" target=\"_blank\" rel=\"noopener\">orchestration layer<\/a> that manages how these pieces work together, especially when more than one agent is involved.<\/p><p>That last point raises a fundamental question in design for a single-agent vs multi-agent scenario.<\/p><ul><li>A single agent handles the entire task itself, which is simpler to build and reason about, but can struggle with tasks that require genuinely different types of expertise or parallel work.<\/li><li>A multi-agent setup splits the task across specialized agents (a research agent, a writing agent, a review agent, for example) coordinated by a supervisor or orchestrator agent.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d57db42 e-con-full section-head-margin-bottom e-flex e-con e-child\" data-id=\"d57db42\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2743596 elementor-widget elementor-widget-heading\" data-id=\"2743596\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\">Multi-agent systems can handle more complex workflows, but they add real coordination overhead and more places where things can go wrong.<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8e8cb90 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"8e8cb90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\tFor most enterprise agents, <a href=\"\/blog\/ai-data-readiness-roadmap-guidance\/\" target=\"_blank\" rel=\"noopener\">access to current, proprietary data<\/a> is what separates genuinely useful systems from ones that sound impressive but produce inaccurate results.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd4c6a6 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"bd4c6a6\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"buildingAgent\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 3: Building the Agent<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b67dc00 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"b67dc00\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>With an architecture in mind, the build stage is where the agent actually gets assembled. This is the heart of how to build AI agents in practice.<\/p><p>Framework choice is one of the first decisions, and it&#8217;s worth approaching without brand loyalty. Options like LangGraph, CrewAI, and AutoGen each take a different approach to orchestration, state management, and multi-agent coordination. None of these is universally \u201cbetter.\u201d The right <a href=\"\/blog\/comparing-agentic-ai-frameworks-key-features-and-benefits\/\" target=\"_blank\" rel=\"noopener\">agentic AI framework<\/a> depends on your use case&#8217;s complexity, your team&#8217;s familiarity with the tooling, and how much control you need over the agent&#8217;s decision path versus how much you&#8217;re comfortable delegating to the framework&#8217;s defaults.<\/p><p>Choosing the right framework is only the beginning. The real work starts when you enable the agent to take action instead of simply suggesting what should be done. It involves wiring up memory so the agent retains the right context without carrying so much history that it becomes slow or unfocused. It involves careful prompt and instruction design, since an agent&#8217;s behavior is only as reliable as the instructions and constraints it&#8217;s operating under.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2b66c04 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"2b66c04\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"evaluation\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 4: Testing, Evaluation, and Guardrails<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-776a347 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"776a347\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Evaluating an agent is more complex than assessing traditional software features due to their non-deterministic nature. With a single prompt, traditional unit tests are of little use, since agents can produce a variety of outputs. Especially with outputs that are not strictly objective, one can use LLM-as-judge methods, where a separate model is used to score the output based on known criteria. While more automated than human review, this method is of limited scalability, requiring continued human review to uphold trust.<br \/><br \/>The evaluation of agents also has to take into account failure modes that agents alone suffer from, like infinite loops from repeated unsuccessful attempts, edge cases that are outside the agent&#8217;s training and instruction, and errors that cascade through a system of multiple agents. Evaluation has to go beyond standard cases and take into account abnormal and faulty behavior.<br \/><br \/>To mitigate agents&#8217; self-operating liabilities, rail guards are employed. They have built-in safety features that limit the agent&#8217;s permission to carry out a task, the agent is mandated to seek approval before carrying out high-risk and\/or irreversible actions, and the agent is structurally limited in its access to the system and the data. For operations that are financial, legal, and\/or customer-facing, a review of a human is mandated at critical points of the process to enable a more secure launch.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25bf235 indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"25bf235\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"deploying\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 5: Deploying to Production and Scaling<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e40b165 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"e40b165\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>This is the \u201cto production\u201d half of the equation, and it&#8217;s where a lot of otherwise well-built <br \/>agents quietly stall.<br \/><br \/>Deployment patterns for agents vary depending on latency and reliability requirements, but the common thread across successful agentic AI deployment is observability. That means tracing the agent&#8217;s actual reasoning and action steps, not just logging final outputs, so that when something goes wrong, your team can see exactly where the agent&#8217;s plan diverged from what was expected. Without this kind of step-by-step visibility, debugging an agent in production is close to impossible.<br \/><br \/>Agentic workflows consisting of multiple agents can use up tokens and compute resources very rapidly, as compared to a single LLM. Because of this, it is especially important to measure the costs of each of these multiple steps alongside the associated success rates. Latency and reliability require equal consideration. Since agent workflows consist of multiple steps, and each step takes time, the team must agree upon what the acceptable delay is for the given use case.<br \/><br \/>Using agentic AI in production continuously is a discipline on its own. This is commonly referred to as AgentOps or LLMOps. Expanding AI agents to new workflows from multiple pre-established workflows cannot be accomplished simply through duplication of the single workflows that were previously built. It means that new agent workflows must be built on previously created structures and systems such as guardrails and observability rather than creating these systems and structures for every single agent workflow.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea19cfc indigo-h3 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"ea19cfc\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"governance\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Stage 6: Governance and Ongoing Management<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2c29a3 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"b2c29a3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Getting an agent into production isn&#8217;t the finish line. Sustaining it responsibly is its own <br \/>ongoing effort.<\/p><p>As agents continue to operate, the integration of AI guardrails will need to become a standard. Evaluation of the risks involved, as well as maintaining an audit and assessment records, will be necessary for agents to act in a transparent way. In addition to risk mitigation, agent governance will need to be a standard. <a href=\"\/blog\/enterprise-ai-agent-security-lessons\/\" target=\"_blank\" rel=\"noopener\">Access controls for agents<\/a> and models will need to be evaluated and managed regularly, not just during the initial implementation. The permissions associated with the agent will also need to be evaluated regularly, as the role of the agent expands.<\/p><p>There will need to be a review of the agent as the business processes that interact with the agent change, and as the tools and models associated with the agent change. The needs of the business will also require a change of the agent. Using a continual improvement model for AI will prevent an agent from degrading in performance in a production environment. Those organizations that view the deployment of agentic AI as a one-time process will tend to have agents that degrade unexpectedly.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6230a17 e-con-full e-grid section-head-margin-bottom e-con e-child\" data-id=\"6230a17\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d1a0b6e elementor-widget elementor-widget-icon-box\" data-id=\"d1a0b6e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tGuardrails as standard\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tAI guardrails become a permanent part of how agents operate.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35be0dc elementor-widget elementor-widget-icon-box\" data-id=\"35be0dc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tRisk and audit records\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tRisk evaluation and audit records keep agent behavior transparent.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d5dece7 elementor-widget elementor-widget-icon-box\" data-id=\"d5dece7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tAccess controls\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tControls for agents and models are managed regularly, not just at launch.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c850c35 elementor-widget elementor-widget-icon-box\" data-id=\"c850c35\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-box.default\">\n\t\t\t\t\t\t\t<div class=\"elementor-icon-box-wrapper\">\n\n\t\t\t\n\t\t\t\t\t\t<div class=\"elementor-icon-box-content\">\n\n\t\t\t\t\t\t\t\t\t<h5 class=\"elementor-icon-box-title\">\n\t\t\t\t\t\t<span  >\n\t\t\t\t\t\t\tPermission reviews\t\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/h5>\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<p class=\"elementor-icon-box-description\">\n\t\t\t\t\t\tPermissions are re-evaluated as the agent's role expands.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4710b31 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"4710b31\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"whyFail\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Why Agentic AI Projects Fail to Reach Production<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e2c05b6 section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"e2c05b6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>A few honest patterns show up again and again in projects that never make it out of <br \/>pilot purgatory.<\/p><p>Choosing the wrong use case, often something too ambitious for a first attempt, sets the whole project up to struggle before the architecture is even decided. Skipping evaluation entirely, and relying on \u201cit looked right when I tested it,\u201d leaves teams blind to failure rates until customers or internal users start reporting problems. Weak guardrails, often added late or skipped under time pressure, mean the agent&#8217;s occasional mistakes can turn into real incidents rather than contained, recoverable ones. Ignoring cost and latency until after launch often results in an agent that&#8217;s technically working but too slow or too expensive to run at real volume.<\/p><p>No observability is another common failure point: without visibility into what the agent is actually doing at each step, debugging becomes guesswork, and trust in the system erodes fast. And perhaps most common of all is what practitioners call <a href=\"\/blog\/enterprise-ai-pilot-to-production\/\" target=\"_blank\" rel=\"noopener\">pilot purgatory<\/a>, where a project stays in perpetual proof-of-concept mode because no one addressed the production requirements (evaluation, guardrails, monitoring, governance) early enough for the agent to graduate. <span class=\"TextRun SCXW229483486 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229483486 BCX0\">Underestimating the effort involved in <\/span><span class=\"NormalTextRun SCXW229483486 BCX0\">data<\/span><span class=\"NormalTextRun SCXW229483486 BCX0\">,<\/span> <span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW229483486 BCX0\">tool<\/span><span class=\"NormalTextRun SCXW229483486 BCX0\"> and <\/span><\/span><a class=\"Hyperlink SCXW229483486 BCX0\" href=\"\/artificial-intelligence\/ai-integration-services\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW229483486 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229483486 BCX0\" data-ccp-charstyle=\"Hyperlink\">AI <\/span><span class=\"NormalTextRun SCXW229483486 BCX0\" data-ccp-charstyle=\"Hyperlink\">integration<\/span><\/span><\/a><span class=\"TextRun SCXW229483486 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW229483486 BCX0\"> rounds out the list. Getting an <\/span><span class=\"NormalTextRun ContextualSpellingAndGrammarErrorV2Themed SCXW229483486 BCX0\">agent<\/span><span class=\"NormalTextRun SCXW229483486 BCX0\"> reliable access to the right systems and data is often a bigger lift than the agent logic itself.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27ed165 section-head-margin-bottom elementor-widget elementor-widget-image\" data-id=\"27ed165\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"860\" height=\"454\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body2.webp\" class=\"attachment-large size-large wp-image-217392\" alt=\"\" srcset=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body2.webp 860w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body2-300x158.webp 300w, https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/Agentic-AI-Implementation-Body2-768x405.webp 768w\" sizes=\"auto, (max-width: 860px) 100vw, 860px\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1329d12 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"1329d12\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"conclusion\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b0abfa section-head-margin-bottom elementor-widget elementor-widget-text-editor\" data-id=\"9b0abfa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Agentic AI implementation isn&#8217;t about building an impressive demo. It&#8217;s about the full journey: choosing a use case with the right scope, designing an architecture that fits the problem, building with guardrails from the start, testing rigorously before anything touches production, deploying with real observability, and governing the system as it evolves. Teams that treat this as a complete journey, rather than a single build phase, are the ones whose agents actually make it into production and stay there.<\/p><p><span class=\"TextRun SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\">If your organization is trying to move an agentic AI initiative from pilot to production, <\/span><\/span><a class=\"Hyperlink SCXW99481846 BCX0\" href=\"\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\" data-ccp-charstyle=\"Hyperlink\">10Pearls<\/span><\/span><\/a><span class=\"TextRun SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\">\u2019 <\/span><\/span><a href=\"\/artificial-intelligence\/agentic-ai-development-services\/\" target=\"_blank\" rel=\"noopener\"><span class=\"TextRun Underlined SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\">agentic AI<\/span><span class=\"NormalTextRun SCXW99481846 BCX0\"> development<\/span><\/span><\/a><span class=\"TextRun SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\"> and <\/span><\/span><a class=\"Hyperlink SCXW99481846 BCX0\" href=\"\/artificial-intelligence\/ai-consulting-services\/\" target=\"_blank\" rel=\"noreferrer noopener\"><span class=\"TextRun Underlined SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\" data-ccp-charstyle=\"Hyperlink\">AI consulting services<\/span><\/span><\/a><span class=\"TextRun SCXW99481846 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW99481846 BCX0\"> teams can help you work through the architecture, build, and governance decisions that make that transition successful.<\/span><\/span><span class=\"EOP Selected SCXW99481846 BCX0\" data-ccp-props=\"{&quot;201341983&quot;:0,&quot;335559740&quot;:276}\">\u00a0<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c5c6de7 indigo-h2 section-title-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"c5c6de7\" data-element_type=\"widget\" data-e-type=\"widget\" id=\"faqs\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51f1720 elementor-widget-mobile__width-initial elementor-widget elementor-widget-n-accordion\" data-id=\"51f1720\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;default_state&quot;:&quot;expanded&quot;,&quot;max_items_expended&quot;:&quot;one&quot;,&quot;n_accordion_animation_duration&quot;:{&quot;unit&quot;:&quot;ms&quot;,&quot;size&quot;:400,&quot;sizes&quot;:[]}}\" data-widget_type=\"nested-accordion.default\">\n\t\t\t\t\t\t\t<div class=\"e-n-accordion\" aria-label=\"Accordion. Open links with Enter or Space, close with Escape, and navigate with Arrow Keys\">\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8590\" class=\"e-n-accordion-item\" open>\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"1\" tabindex=\"0\" aria-expanded=\"true\" aria-controls=\"e-n-accordion-item-8590\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> How do you implement agentic AI? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8590\" class=\"elementor-element elementor-element-b5ff1b8 e-con-full e-flex e-con e-child\" data-id=\"b5ff1b8\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-678ff15 elementor-widget elementor-widget-text-editor\" data-id=\"678ff15\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Implementing Agentic AI is a multi-step process. It involves selecting a use case at a high value but limited scope, designing the architecture by including the appropriate components and design patterns, building the agent with the guardrails in place, performing extensive testing using real-world examples, deploying the agent with the ability to monitor its usage, and implementing control to ensure its compliance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8591\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"2\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-8591\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What are the stages of agentic AI implementation? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8591\" class=\"elementor-element elementor-element-fac6958 e-con-full e-flex e-con e-child\" data-id=\"fac6958\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e067c76 elementor-widget elementor-widget-text-editor\" data-id=\"e067c76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>The typical stages are use case selection, architecture and design, building the agent, testing and evaluation, deployment and scaling, and ongoing governance. Skipping or rushing any of these stages, especially evaluation and guardrails, is a common reason agentic AI projects stall before reaching production.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8592\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"3\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-8592\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What's the difference between agentic AI and AI agents? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8592\" class=\"elementor-element elementor-element-1e5307f e-con-full e-flex e-con e-child\" data-id=\"1e5307f\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0dccf9e elementor-widget elementor-widget-text-editor\" data-id=\"0dccf9e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>\u201cAgentic AI\u201d describes the underlying approach: autonomous, goal-driven systems that plan and act. \u201cAI agents\u201d typically refers to the individual implementations of that approach, the specific systems built to handle a task. In practice, the terms are often used interchangeably in enterprise contexts.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8593\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"4\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-8593\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> What frameworks are used to build agentic AI? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8593\" class=\"elementor-element elementor-element-f994cac e-con-full e-flex e-con e-child\" data-id=\"f994cac\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e448b53 elementor-widget elementor-widget-text-editor\" data-id=\"e448b53\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>LangGraph, CrewAI, and AutoGen are popular agentic AI tools. Each offers distinctive advantages regarding orchestration, multi-agent coordination, and various control aspects. The degree of complexity with the use case and the degree of control over the agent\u2019s decision-making process that the team requires will determine which tool is the most appropriate.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8594\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"5\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-8594\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> How do you get an agentic AI system into production? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8594\" class=\"elementor-element elementor-element-827bd34 e-con-full e-flex e-con e-child\" data-id=\"827bd34\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0cca985 elementor-widget elementor-widget-text-editor\" data-id=\"0cca985\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Getting an agentic AI system into production requires rigorous evaluation against real task scenarios, strong guardrails and permission controls, observability into each step of the agent&#8217;s reasoning, and monitoring for cost, latency, and reliability. This is typically where agentic AI projects either succeed or stall.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t<details id=\"e-n-accordion-item-8595\" class=\"e-n-accordion-item\" >\n\t\t\t\t<summary class=\"e-n-accordion-item-title\" data-accordion-index=\"6\" tabindex=\"-1\" aria-expanded=\"false\" aria-controls=\"e-n-accordion-item-8595\" >\n\t\t\t\t\t<span class='e-n-accordion-item-title-header'><h3 class=\"e-n-accordion-item-title-text\"> Why do agentic AI projects fail? <\/h3><\/span>\n\t\t\t\t\t\t\t<span class='e-n-accordion-item-title-icon'>\n\t\t\t<span class='e-opened' ><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-minus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h384c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t\t<span class='e-closed'><svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-plus\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 208H272V64c0-17.67-14.33-32-32-32h-32c-17.67 0-32 14.33-32 32v144H32c-17.67 0-32 14.33-32 32v32c0 17.67 14.33 32 32 32h144v144c0 17.67 14.33 32 32 32h32c17.67 0 32-14.33 32-32V304h144c17.67 0 32-14.33 32-32v-32c0-17.67-14.33-32-32-32z\"><\/path><\/svg><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t<div role=\"region\" aria-labelledby=\"e-n-accordion-item-8595\" class=\"elementor-element elementor-element-ee98497 e-con-full e-flex e-con e-child\" data-id=\"ee98497\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3064f18 elementor-widget elementor-widget-text-editor\" data-id=\"3064f18\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p>Considering the following factors often leads to project failure. These factors include selecting a first use case that has far too much scope, avoidance of testing and evaluation, setting of weak and late guardrails, a complete absence of the ability to monitor agent behavior, cost and latency being of no concern until after the agent has been developed, and the amount of work required to integrate the required data and tools being grossly miscalculated.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-88861df e-con-full e-flex e-con e-child\" data-id=\"88861df\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9f9880e elementor-widget elementor-widget-heading\" data-id=\"9f9880e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">TABLE OF CONTENTS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16b402e elementor-widget elementor-widget-heading\" data-id=\"16b402e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#whatAgentic\">What Agentic AI Implementation Actually Involves<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e8048d9 elementor-widget elementor-widget-heading\" data-id=\"e8048d9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#useCase\">Stage 1: Choosing the Right Use Case<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d98dce elementor-widget elementor-widget-heading\" data-id=\"1d98dce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#designAgent\">Stage 2: Designing the Agent \u2014 Architecture and Patterns<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e856fb2 elementor-widget elementor-widget-heading\" data-id=\"e856fb2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#buildingAgent\">Stage 3: Building the Agent<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8100e97 elementor-widget elementor-widget-heading\" data-id=\"8100e97\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#evaluation\">Stage 4: Testing, Evaluation, and Guardrails<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0976db5 elementor-widget elementor-widget-heading\" data-id=\"0976db5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#deploying\">Stage 5: Deploying to Production and Scaling<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c29d083 elementor-widget elementor-widget-heading\" data-id=\"c29d083\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#governance\">Stage 6: Governance and Ongoing Management<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9537f76 elementor-widget elementor-widget-heading\" data-id=\"9537f76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#whyFail\">Why Agentic AI Projects Fail to Reach Production<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be9261f elementor-widget elementor-widget-heading\" data-id=\"be9261f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#conclusion\">Conclusion<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5b612cc elementor-widget elementor-widget-heading\" data-id=\"5b612cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<p class=\"elementor-heading-title elementor-size-default\"><a href=\"#faqs\">FAQs<\/a><\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f14881d section-padding e-flex e-con-boxed e-con e-parent\" data-id=\"f14881d\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6351b30 indigo-h2 section-head-margin-bottom elementor-widget elementor-widget-heading\" data-id=\"6351b30\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related blogs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4b4e8e6 elementor-widget elementor-widget-insights_section_widget\" data-id=\"4b4e8e6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"insights_section_widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>\n\t\t\/* \u2500\u2500 Widget wrapper \u2500\u2500 *\/\n\t\t#isw-4b4e8e6 {\n\t\t\tposition: relative;\n\t\t\tpadding: 0 40px; \/* space for arrows on left\/right *\/\n\t\t\tbox-sizing: border-box;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-outer {\n\t\t\tposition: relative;\n\t\t\toverflow: hidden;\n\t\t\tpadding: 20px 0;\n\t\t\tmargin: -20px 0;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-track {\n\t\t\tdisplay: flex;\n\t\t\tflex-direction: row;\n\t\t\tflex-wrap: nowrap;\n\t\t\tgap: 20px;\n\t\t\ttransition: transform .5s cubic-bezier(.25,.46,.45,.94);\n\t\t\twill-change: transform;\n\t\t\talign-items: stretch;\n\t\t\tvisibility: hidden;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-slide { flex-shrink: 0; box-sizing: border-box; display: flex; }\n\n\t\t\/* \u2500\u2500 Card \u2014 no shadow by default, only on hover \u2500\u2500 *\/\n\t\t#isw-4b4e8e6 .isw-card {\n\t\t\tborder-radius: 16px; overflow: hidden; width: 100%; flex: 1;\n\t\t\tposition: relative; height: 380px; background: #fff;\n\t\t\tbox-shadow: none;\n\t\t\ttransition: box-shadow .3s ease;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-card:hover {\n\t\t\tbox-shadow: 0px 14px 36px -8px rgba(99,102,241,0.2);\n\t\t}\n\n\t\t\/* Layer 1 \u2014 Image *\/\n\t\t#isw-4b4e8e6 .isw-img-wrap {\n\t\t\tposition: absolute; top: 0; left: 0;\n\t\t\twidth: 100%; height: 100%; z-index: 1;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-img-wrap img {\n\t\t\twidth: 100%; height: 100%; display: block; object-fit: cover;\n\t\t}\n\n\t\t\/* Layer 2 \u2014 Text bar *\/\n\t\t#isw-4b4e8e6 .isw-text-wrap {\n\t\t\tposition: absolute; bottom: 0; left: 0;\n\t\t\twidth: 100%; height: 35%; z-index: 2;\n\t\t\tbackground: #fff; padding: 16px 24px 20px;\n\t\t\tbox-sizing: border-box; display: flex; flex-direction: column;\n\t\t\tgap: 8px; overflow: hidden;\n\t\t\ttransition: height .35s cubic-bezier(.4,0,.2,1);\n\t\t}\n\t\t#isw-4b4e8e6 .isw-card:hover .isw-text-wrap { height: 100%; }\n\n\t\t#isw-4b4e8e6 .isw-excerpt-wrap,\n\t\t#isw-4b4e8e6 .isw-readmore-wrap { display: none; }\n\t\t#isw-4b4e8e6 .isw-card:hover .isw-excerpt-wrap { display: block; }\n\t\t#isw-4b4e8e6 .isw-card:hover .isw-readmore-wrap { 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*\/\n\t\t#isw-4b4e8e6 .isw-dots {\n\t\t\tdisplay: flex;\n\t\t\talign-items: center;\n\t\t\tjustify-content: center;\n\t\t\tgap: 16px;\n\t\t\tmargin-top: 32px;\n\t\t\theight: 36px;\n\t\t\tbox-sizing: content-box;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-dot {\n\t\t\twidth: 10px !important;\n\t\t\theight: 10px !important;\n\t\t\tborder-radius: 50% !important;\n\t\t\tbackground: var(--e-global-color-f527530) !important;\n\t\t\topacity: 0.25;\n\t\t\tcursor: pointer;\n\t\t\tborder: none !important;\n\t\t\tpadding: 0;\n\t\t\ttransition: width .25s ease, height .25s ease, opacity .25s ease, background .25s ease, border .25s ease;\n\t\t\tflex-shrink: 0;\n\t\t\tdisplay: block;\n\t\t\tbox-sizing: border-box;\n\t\t}\n\t\t#isw-4b4e8e6 .isw-dot.active {\n\t\t\twidth: 14px !important;\n\t\t\theight: 14px !important;\n\t\t\topacity: 1 !important;\n\t\t\tbackground: var(--e-global-color-5d532a8) !important;\n\t\t\tborder: 3px solid var(--e-global-color-f527530) !important;\n\t\t}\n\n\t\t\/* Read More *\/\n\t\t#isw-4b4e8e6 .isw-readmore {\n\t\t\tfont-size: 14px; font-weight: 600;\n\t\t\ttext-decoration: none; color: #171616 !important;\n\t\t\tborder-bottom: 1px solid #171616;\n\t\t\tdisplay: inline-block;\n\t\t}\n\t\t<\/style><div id=\"isw-4b4e8e6\" class=\"isw-v2\"><button class=\"isw-prev\" aria-label=\"Previous\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M15.75 19.5L8.25 12L15.75 4.5\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button class=\"isw-next\" aria-label=\"Next\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M8.25 4.5L15.75 12L8.25 19.5\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><div class=\"isw-outer\"><div class=\"isw-track\"><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-vs-ml-vs-deep-learning\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/09\/AI-vs-Machine-learning-Featured-1024x1024.webp\" alt=\"AI vs ML vs Deep Learning an Enterprise Guide\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-vs-ml-vs-deep-learning\/\">AI vs ML vs Deep Learning an Enterprise Guide<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Synthetic identity fraud is the fastest growing financial crime in the US. Understanding why that is and what its life...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/ai-vs-ml-vs-deep-learning\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/how-to-measure-ai-roi-enterprise-framework\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/Measuring-AI-Investments-ROI-Featured-1024x1024.webp\" alt=\"Measuring AI Investments\u2019 ROI | Framework for Enterprise Leaders\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/how-to-measure-ai-roi-enterprise-framework\/\">Measuring AI Investments\u2019 ROI | Framework for Enterprise Leaders<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Learn how to measure AI ROI with a practical framework covering cost savings, revenue growth, risk reduction, productivity, strategic value,...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/how-to-measure-ai-roi-enterprise-framework\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-implementation\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/Agentic-AI-Implementation-Social.webp\" alt=\"Agentic AI Implementation: How to Build AI Agents\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-implementation\/\">Agentic AI Implementation: How to Build AI Agents<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Turn agentic AI from an experimental concept into a production-ready capability with guidance on architecture, development, evaluation, deployment, observability, and...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-implementation\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/what-is-agentic-ai\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/Agentic-Ai-Blog-Featured-1024x1024.webp\" alt=\"What Is Agentic AI?\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/what-is-agentic-ai\/\">What Is Agentic AI?<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Take agentic AI from promising idea to production-ready capability with a practical framework for building reliable agents, managing risk, and...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/what-is-agentic-ai\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-integration-strategy-enterprise-framework\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/Building-an-AI-Integration-Strategy-Blog-Thumbnail-1024x1024.webp\" alt=\"Building an AI Integration Strategy\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-integration-strategy-enterprise-framework\/\">Building an AI Integration Strategy<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Learn a practical 9-step AI integration framework to define outcomes, overcome organizational barriers, measure ROI, and build AI solutions that...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/ai-integration-strategy-enterprise-framework\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-agent-authorization\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/AI-Agent-Authorization-Featured-1024x1024.webp\" alt=\"AI Agent Authorization: Governing Autonomous AI\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-agent-authorization\/\">AI Agent Authorization: Governing Autonomous AI<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">As AI agents are granted more autonomy across enterprise systems, organizations need to define what agents can access and what...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/ai-agent-authorization\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-telecom\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/AI-in-Telecom-Featured-1024x1024.webp\" alt=\"Agentic AI in the Telecom Industry\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-telecom\/\">Agentic AI in the Telecom Industry<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">The telecom industry is embracing agentic AI for multiple operational and customer-facing use cases, while navigating legacy systems, integration, and...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/agentic-ai-telecom\/\">Read More<\/a><\/div><\/div><\/div><\/div><div class=\"isw-slide\"><div class=\"isw-card\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-real-estate-industry\/\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;z-index:1;display:block;text-decoration:none;\"><div class=\"isw-img-wrap\"><img decoding=\"async\" src=\"https:\/\/10pearls.com\/uk\/wp-content\/uploads\/2026\/08\/AI-in-real-estate-thumbnail-1024x1024.webp\" alt=\"How is AI being used in real estate in 2026\" loading=\"lazy\"><\/div><\/a><div class=\"isw-text-wrap\"><p class=\"isw-tag\">AI\/ML<\/p><p class=\"isw-title\"><a href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-real-estate-industry\/\">How is AI being used in real estate in 2026<\/a><\/p><div class=\"isw-excerpt-wrap\"><p class=\"isw-excerpt\">Discover how AI is transforming real estate, from asset management to contract intelligence, with practical use cases and signs that...<\/p><\/div><div class=\"isw-readmore-wrap\" style=\"text-align:left\"><a class=\"isw-readmore\" href=\"https:\/\/10pearls.com\/uk\/blog\/ai-in-real-estate-industry\/\">Read More<\/a><\/div><\/div><\/div><\/div><\/div><\/div><div class=\"isw-dots\"><\/div><\/div><script>(function(){\n\t\t\tvar id       = \"isw-4b4e8e6\";\n\t\t\tvar spvDesk  = 3;\n\t\t\tvar spvTab   = 2;\n\t\t\tvar gap      = 20;\n\t\t\tvar total    = 8;\n\t\t\tvar autoplay = true;\n\t\t\tvar autoSpd  = 5000;\n\t\t\tvar showDots = true;\n\n\t\t\tvar wrap  = document.getElementById(id);\n\t\t\tif(!wrap) return;\n\t\t\tvar outer  = wrap.querySelector(\".isw-outer\");\n\t\t\tvar track  = wrap.querySelector(\".isw-track\");\n\t\t\tvar slides = wrap.querySelectorAll(\".isw-slide\");\n\t\t\tvar dotsWrap = wrap.querySelector(\".isw-dots\");\n\t\t\tvar btnPrev  = wrap.querySelector(\".isw-prev\");\n\t\t\tvar btnNext  = 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