{"id":23321,"date":"2026-09-24T06:40:58","date_gmt":"2026-09-24T06:40:58","guid":{"rendered":"https:\/\/www.sphinx-solution.com\/blog\/?p=23321"},"modified":"2026-09-24T06:40:58","modified_gmt":"2026-09-24T06:40:58","slug":"enterprise-ai-agent-architecture","status":"publish","type":"post","link":"https:\/\/www.sphinx-solution.com\/blog\/enterprise-ai-agent-architecture\/","title":{"rendered":"Enterprise AI Agent Architecture: Components, Workflows &#038; Best Practices"},"content":{"rendered":"<div style=\"text-align: left; background-color: #ebf5ff; padding: 20px 25px; font-size: 16px; border-radius: 16px; border: 1px solid #b6dcff; width: 100%; box-sizing: border-box;\">\n<p style=\"font-size: 16px; margin: 0 0 15px 0;\"><strong>Key Takeaways<\/strong><\/p>\n<ul style=\"margin: 0; padding-left: 20px; line-height: 1.7;\">\n<li style=\"margin-bottom: 10px;\"><span class=\"TextRun SCXW128607035 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW128607035 BCX8\">AI agent architecture is more than an LLM: Enterprise agents use reasoning, memory, tools, data, orchestration, security, and observability.\u00a0<\/span><\/span><\/li>\n<li style=\"margin-bottom: 10px;\"><span class=\"TextRun SCXW149848744 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW149848744 BCX8\">Knowing the scope of an AI agent application and use case will help you choose the best architecture.\u00a0<\/span><\/span><\/li>\n<li style=\"margin-bottom: 10px;\"><span class=\"TextRun SCXW23741245 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW23741245 BCX8\">Enterprise workflows require controlled autonomy: Permissions, human approvals, validation, and governance are used to make sure the agents\u00a0<\/span><span class=\"NormalTextRun SCXW23741245 BCX8\">operate<\/span><span class=\"NormalTextRun SCXW23741245 BCX8\">\u00a0safely and reliably.<\/span><\/span><\/li>\n<li style=\"margin-bottom: 10px;\"><span class=\"TextRun SCXW220065285 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW220065285 BCX8\">Memory, tools, and enterprise data drive agent effectiveness: Connecting agents to trusted knowledge and business systems enables them to move beyond simple conversations.<\/span><\/span><\/li>\n<li><span class=\"TextRun SCXW86912458 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW86912458 BCX8\">Production-ready agents need continuous monitoring: Observability, evaluation, security, cost tracking, and failure handling are essential for scaling AI agents across an enterprise.<\/span><\/span><\/li>\n<\/ul>\n<\/div>\n<p><span data-contrast=\"none\">Connecting an LLM to a chatbot interface and calling it an AI agent is how most failed enterprise agent projects start. It works in a demo. It falls apart the first time the agent needs to check real account data, call an internal API, or\u00a0make a decision\u00a0that\u00a0actually matters.\u00a0<\/span><b><span data-contrast=\"none\">AI agent architecture<\/span><\/b><span data-contrast=\"none\">\u00a0is what separates a working prototype from a system that can be trusted with production workflows.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">An enterprise AI agent is not just a model.\u00a0It&#8217;s\u00a0a system made up of interconnected components: reasoning, planning, memory, tool access, data grounding, orchestration, security, and observability, all working together. Get the architecture right, and the model becomes one\u00a0component\u00a0among several. Skip it, and the model is carrying weight it was never designed to carry alone.<\/span><\/p>\n<h2 id=\"what-is-ai-agent-architecture\"><strong><span class=\"TextRun SCXW162688347 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW162688347 BCX8\" data-ccp-parastyle=\"heading 2\">What is AI Agent Architecture?<\/span><\/span><span class=\"EOP Selected SCXW162688347 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><span data-contrast=\"none\">Architecting an AI agent is the way an agent&#8217;s reasoning model is coupled with the context, tools, and controls it\u00a0requires\u00a0to work reliably toward a goal rather than produce just a single answer. On the other hand, it also specifies how an agent receives a goal, reasons and plans, assesses context, chooses tools, acts, assesses outcomes, updates its context, and either continues or\u00a0terminates\u00a0its work.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">That&#8217;s\u00a0a different question from what an AI agent is conceptually.\u00a0It&#8217;s\u00a0a question of how the pieces are\u00a0actually built\u00a0and wired together in a system that\u00a0has to\u00a0run in production, get\u00a0monitored, and be trusted with real enterprise data and actions.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"enterprise-ai-agent-architecture-high-level-overview\"><strong><span class=\"TextRun SCXW17540585 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW17540585 BCX8\" data-ccp-parastyle=\"heading 2\">Enterprise AI Agent Architecture: High-Level Overview<\/span><\/span><span class=\"EOP Selected SCXW17540585 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><span class=\"TextRun SCXW205930906 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW205930906 BCX8\">A practical reference model for how the layers stack:<\/span><\/span><span class=\"EOP Selected SCXW205930906 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-23325 size-full\" src=\"https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/Enterprise-AI-Agent-Architecture_-High-Level-Overview_.webp\" alt=\"Enterprise AI Agent Architecture: High-Level Overview\" width=\"700\" height=\"373\" srcset=\"https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/Enterprise-AI-Agent-Architecture_-High-Level-Overview_.webp 700w, https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/Enterprise-AI-Agent-Architecture_-High-Level-Overview_-300x160.webp 300w, https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/Enterprise-AI-Agent-Architecture_-High-Level-Overview_-390x208.webp 390w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p>\n<p><span class=\"TextRun SCXW31018233 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW31018233 BCX8\">Each layer depends on the one below it. The reasoning layer is only as good as the context memory feeds it. Planning only works if orchestration can\u00a0<\/span><span class=\"NormalTextRun AdvancedProofingIssueV2Themed SCXW31018233 BCX8\">actually route<\/span><span class=\"NormalTextRun SCXW31018233 BCX8\">\u00a0tasks to the right tools. And none of it is safe to run in production without governance and observability wrapped around the\u00a0<\/span><span class=\"NormalTextRun SCXW31018233 BCX8\">whole system<\/span><span class=\"NormalTextRun SCXW31018233 BCX8\">, not just the model at the centre.<\/span><\/span><span class=\"EOP Selected SCXW31018233 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"what-are-the-core-components-of-ai-agent-architecture\"><strong><span class=\"TextRun SCXW168465190 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW168465190 BCX8\" data-ccp-parastyle=\"heading 2\">What are the core components of AI Agent Architecture?<\/span><\/span><span class=\"EOP Selected SCXW168465190 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><b><span data-contrast=\"none\">Agent\/reasoning layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">The LLM interprets the task, decomposes goals, and makes decisions. This is only part of the system, not the entire agent, regardless of how competent the model is on its own.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Planning and orchestration layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Handles task decomposition, workflow sequencing, retries, routing between tools or sub-agents, and state management across a multi-step task. This layer matters most in enterprise environments where a single request often triggers several dependent actions.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Memory and context layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Covers short-term task state, longer-term memory across sessions, and retrieval from vector databases where relevant. Memory design directly affects reliability, cost, privacy, and latency, since every piece of context carried forward is also a piece of context the model\u00a0has to\u00a0process, and a business\u00a0has to\u00a0secure.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Tool and integration layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">How the agent connects to APIs, CRMs, ERPs, internal applications, and SaaS platforms, typically through function or tool calling with explicitly scoped permissions. This is where controlled access\u00a0actually gets\u00a0enforced, not just described in a policy document.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Knowledge and data layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Information from enterprise data, corporate knowledge bases, retrieval-augmented generation (RAG), structured data stores, and live data feeds. It is the grounding of the agent in enterprise data that stops it from being confidently wrong.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Communication layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">How the agent exchanges information with users, applications, other agents, and enterprise systems. When a workflow involves several specialised agents coordinating on the same task, this layer becomes the backbone of the system, a topic covered in more depth in our guide to multi-agent systems.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Security and governance layer:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Authentication,\u00a0authorisation\u00a0and role-based access; data privacy controls; secrets management; audit logs; policy enforcement. Agentic systems require more robust controls than traditional chat applications as they can take actions, not just generate text.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">Observability and monitoring:<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Logs, traces, tool call records, latency, token usage, failure rates, hallucination tracking, and task success rate. Without this layer, a production agent is\u00a0essentially a\u00a0black box, and troubleshooting becomes guesswork.<\/span><\/p>\n<h2 id=\"how-an-enterprise-ai-agent-workflow-works\"><strong><span class=\"TextRun SCXW4550927 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW4550927 BCX8\" data-ccp-parastyle=\"heading 2\">How an Enterprise AI Agent Workflow Works<\/span><\/span><span class=\"EOP Selected SCXW4550927 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:80}\">\u00a0<\/span><\/strong><\/h2>\n<p><span class=\"TextRun SCXW200754628 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW200754628 BCX8\">The production-grade AI agent workflow looks like this:\u00a0<\/span><\/span><span class=\"EOP Selected SCXW200754628 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-23326 size-full\" src=\"https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/How-an-Enterprise-AI-Agent-Workflow-Works_.webp\" alt=\"Enterprise AI Agent Workflow \" width=\"700\" height=\"373\" srcset=\"https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/How-an-Enterprise-AI-Agent-Workflow-Works_.webp 700w, https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/How-an-Enterprise-AI-Agent-Workflow-Works_-300x160.webp 300w, https:\/\/www.sphinx-solution.com\/blog\/wp-content\/uploads\/2026\/09\/How-an-Enterprise-AI-Agent-Workflow-Works_-390x208.webp 390w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/p>\n<h2 id=\"what-are-the-common-ai-agent-architecture-patterns\"><strong><span class=\"TextRun SCXW58520063 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW58520063 BCX8\" data-ccp-parastyle=\"heading 2\">What are the Common AI Agent Architecture Patterns?<\/span><\/span><span class=\"EOP Selected SCXW58520063 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<table data-tablestyle=\"MsoNormalTable\" data-tablelook=\"1696\" aria-rowcount=\"7\" aria-colcount=\"3\">\n<tbody>\n<tr aria-rowindex=\"1\">\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">Pattern<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:2,&quot;335551620&quot;:2,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">How it works<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:2,&quot;335551620&quot;:2,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">Best fit<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335551550&quot;:2,&quot;335551620&quot;:2,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"2\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Single-agent<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">One agent manages the full workflow, tools and all<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Contained, moderately complex tasks<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"3\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Planner-executor<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">One component plans the steps, another executes them<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Multi-step tasks where planning and execution benefit from separation<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"4\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Router-based<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">A routing layer sends requests to specialized agents or tools<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">High-volume, varied request types<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"5\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Multi-agent<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Several specialized agents collaborate on distinct parts of a task<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Workflows spanning genuinely different domains<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"6\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Human-in-the-loop<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Human approval is required before high-risk or sensitive actions<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Workflows where mistakes carry real cost<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"7\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Event-driven<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Agents respond to enterprise events rather than only direct requests<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Monitoring, alerting, and automated remediation workflows<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span class=\"TextRun SCXW254742540 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW254742540 BCX8\">Most enterprise deployments start with a single-agent or planner-executor pattern and only move to multi-agent architecture once a workflow genuinely spans distinct specialities, a distinction covered in detail in our\u00a0<\/span><\/span><span class=\"TextRun SCXW254742540 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW254742540 BCX8\">multi-agent systems guide.<\/span><\/span><span class=\"EOP Selected SCXW254742540 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"ai-agent-architecture-vs-traditional-application-architecture\"><strong><span class=\"TextRun SCXW127846661 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW127846661 BCX8\" data-ccp-parastyle=\"heading 2\">AI Agent Architecture vs Traditional Application Architecture<\/span><\/span><span class=\"EOP Selected SCXW127846661 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<table data-tablestyle=\"MsoNormalTable\" data-tablelook=\"1696\" aria-rowcount=\"9\" aria-colcount=\"3\">\n<tbody>\n<tr aria-rowindex=\"1\">\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">Area<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">Traditional application<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"69905\"><b><span data-contrast=\"none\">AI agent architecture<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"2\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Decision logic<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Mostly predefined<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Model-assisted, more dynamic<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"3\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Workflow<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Explicit, fixed paths<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Can adapt based on context<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"4\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Data access<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Programmed integrations<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Tool and function-based access<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"5\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">State<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Application state<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Context, memory, and task state combined<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"6\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Errors<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Code exceptions<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Tool failures, model errors, reasoning mistakes<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"7\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Testing<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Mostly deterministic<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Requires ongoing evaluation<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"8\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Observability<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Application logs<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Logs, traces, and model or tool behaviour<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<tr aria-rowindex=\"9\">\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Governance<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Access controls<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<td data-celllook=\"4369\"><span data-contrast=\"none\">Access, model, and action governance combined<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span class=\"TextRun SCXW45035589 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW45035589 BCX8\">Enterprise agents generally sit on top of and interact with existing enterprise architecture rather than replacing it. The database, the CRM, the ERP &#8211; none of that goes away. The agent becomes a new consumer of those systems, with its own layer of controls wrapped around it.<\/span><\/span><span class=\"EOP Selected SCXW45035589 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"what-are-the-best-practices-for-enterprise-ai-agent-architecture\"><strong><span class=\"TextRun SCXW238015252 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW238015252 BCX8\" data-ccp-parastyle=\"heading 2\">What are the Best Practices for Enterprise AI Agent Architecture?<\/span><\/span><span class=\"EOP Selected SCXW238015252 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"7\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Begin with a well-scoped business result before you select any architecture, as the pattern should never drive the problem.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"8\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Keep agent permissions tight, and agents should only have access to tools and data relevant to a particular task; having loose access is the quickest route to having a helpful agent go bad.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"9\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Use deterministic logic wherever it&#8217;s sufficient and reserve model reasoning for the parts of the task that genuinely need it.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"10\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Separate planning from execution when a workflow has enough steps that debugging a combined process becomes difficult.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"11\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Design memory intentionally rather than defaulting to &#8220;remember everything,&#8221; since every piece of retained context is also a cost and a privacy consideration.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"12\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Ground actions in trusted enterprise data instead of relying on model knowledge alone.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"13\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Make human approval a part of every process when the cost of a mistake is high or difficult to undo.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"14\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">An experiment or case study can be misleading. Build observability into your processes from the start instead of bringing it in after an attack.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"15\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Design explicitly for failure and recovery, since tool calls and API dependencies will fail eventually.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"16\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Test agents with real-world tasks, not just prompt-hard case studies.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"17\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Keep an eye on cost and latency at all times, not only during the launch cycle.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"18\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Version prompts, tools and agent configs are the same as with any other production code.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"19\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Steer clear of multi-agent complexity, unless the workflow truly needs the need for specialisation.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<h2 id=\"what-are-the-common-architecture-mistakes\"><strong><span class=\"TextRun SCXW121601963 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW121601963 BCX8\" data-ccp-parastyle=\"heading 2\">What are the Common Architecture Mistakes?<\/span><\/span><span class=\"EOP Selected SCXW121601963 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><span data-contrast=\"none\">The recurring failure pattern is noticed across most troubled agent deployments:\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"20\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Treating the LLM as the entire agent instead of one component in a larger system, granting agents broader permissions than the task requires, reaching for multi-agent architecture when a single well-scoped agent would have worked, and skipping failure handling entirely, so the first API timeout takes down the whole workflow.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"21\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Poor memory design shows up as agents that either forget critical context or carry far more of it than they need.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"22\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Missing human approval on sensitive actions, no observability, and relying entirely on prompt instructions for security are three separate ways teams discover, usually the hard way, that a prompt is not a permission boundary.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"23\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Connecting too many tools without a clear reason, ignoring latency and cost until they become a problem, and deploying without defined evaluation criteria round out the list.\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">None of these is exotic failures. They&#8217;re the predictable result of skipping architecture in favour of moving fast.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"how-to-choose-the-right-ai-agent-architecture\"><strong><span class=\"TextRun SCXW18826619 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW18826619 BCX8\" data-ccp-parastyle=\"heading 2\">How to Choose the Right AI Agent Architecture?<\/span><\/span><span class=\"EOP Selected SCXW18826619 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><span data-contrast=\"none\">Match the pattern to the actual shape of the workflow:<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"24\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Simple, predictable workflow \u2192 single agent with tools\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"25\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Complex sequential workflow \u2192 planner-executor\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"26\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Multiple specialised domains \u2192 multi-agent\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"27\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">High-risk workflow \u2192 agent with human-in-the-loop\u00a0<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li aria-setsize=\"-1\" data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"28\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"none\">Event-driven enterprise process \u2192 event-driven agent<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"none\">Task complexity, the quantity of tools involved, the variety of sources, variability of workflow, level of risk, degree of independence, latency requirements, and compliance concerns all tend to favour one design or the other. The design is also not necessarily fixed. Many enterprise deployments begin with a single agent and graduate to a planner-executor or multi-agent arrangement as the actual complexity of the workflow becomes evident in practice.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"conclusion\"><strong><span class=\"TextRun SCXW268390763 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW268390763 BCX8\" data-ccp-parastyle=\"heading 2\">Conclusion<\/span><\/span><span class=\"EOP Selected SCXW268390763 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><span data-contrast=\"none\">Treat your enterprise AI agent as production software, not an LLM front end with a better name. What actually scales is a system architecture that integrates reasoning, data, tools, orchestration, memory, security, and observability, each doing its own thing, with governance as the architecture rather than an add-on.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Sphinx Solutions works with enterprise teams on exactly this: identifying which workflows are genuinely ready for an agent, designing the architecture around the actual risk and complexity involved, and integrating it with the systems that are already running the business. If you&#8217;re past the &#8220;what is an AI agent&#8221; stage and into the &#8220;how do we actually build this safely&#8221; stage, that&#8217;s the conversation worth having next.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<h2 id=\"faqs\"><strong><span class=\"TextRun SCXW244478025 BCX8\" lang=\"EN-GB\" xml:lang=\"EN-GB\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW244478025 BCX8\" data-ccp-parastyle=\"heading 2\">FAQ\u2019s:<\/span><\/span><span class=\"EOP Selected SCXW244478025 BCX8\" data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;134245418&quot;:true,&quot;134245529&quot;:true,&quot;335559738&quot;:360,&quot;335559739&quot;:120}\">\u00a0<\/span><\/strong><\/h2>\n<p><b><span data-contrast=\"none\">What is AI agent architecture?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">AI agent architecture is the structure connecting a reasoning model to the memory, tools, data, and controls it needs to pursue a goal reliably across multiple steps, rather than just generate a single response.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">What are the main components of an AI agent architecture?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">The core components are the reasoning layer, planning and orchestration, memory and context, tool and integration access, a knowledge or data layer, communication, and security, governance, and observability wrapped around all of it.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">How does an enterprise AI agent work?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">It receives a goal, gathers relevant context, plans a sequence of steps, selects and calls the appropriate tools, executes actions, evaluates the results, and either continues, retries, or escalates to a human depending on what it finds.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">What&#8217;s the difference between AI agent architecture and traditional software architecture?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Traditional applications follow mostly predefined logic and fixed workflows, while AI agent architecture adds model-assisted decision-making, adaptive workflows, and governance that has to account for reasoning and tool-use failures, not just code exceptions.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">What architecture is best for enterprise AI agents?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">There&#8217;s no universal answer; the right pattern depends on task complexity, number of tools, risk level, and required autonomy. Simple workflows suit a single agent, while complex or high-risk ones often need planner-executor, multi-agent, or human-in-the-loop designs.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">How do AI agents use enterprise APIs and tools?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Agents access enterprise systems through function or tool calling, where each tool is explicitly defined and scoped, so the agent can only take actions it&#8217;s been specifically granted permission to take.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">How do you secure an enterprise AI agent?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">Through layered controls: authentication and least-privilege authorisation, scoped tool permissions, audit logging, human approval for high-risk actions, output validation, and ongoing monitoring, treated as core architecture rather than an afterthought.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"none\">When should an enterprise use a multi-agent architecture?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"none\">When a workflow genuinely spans distinct specialised domains that benefit from separate agents working in parallel, not simply because a multi-agent system sounds more sophisticated than a single well-scoped agent.<\/span><span data-ccp-props=\"{&quot;134233117&quot;:false,&quot;134233118&quot;:false,&quot;335559738&quot;:0,&quot;335559739&quot;:0}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Key Takeaways AI agent architecture is more than an LLM: Enterprise agents use reasoning, memory, tools, data, orchestration, security, and observability.\u00a0 Knowing the scope of an AI agent application and&hellip;\n","protected":false},"author":21,"featured_media":23323,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"ub_ctt_via":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-23321","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.9 - aioseo.com -->\n\t<meta name=\"description\" content=\"A practical guide to AI agent architecture: core components, workflow patterns, security, and how to choose the right design for your enterprise.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Shaili Gupta\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/www.sphinx-solution.com\/blog\/enterprise-ai-agent-architecture\/\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 4.9.9\" \/>\n\t\t<meta property=\"og:locale\" content=\"en_US\" \/>\n\t\t<meta property=\"og:site_name\" content=\"Software Development, AI &amp; 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