{"id":311,"date":"2026-05-16T09:00:08","date_gmt":"2026-05-16T09:00:08","guid":{"rendered":"https:\/\/blog-origin.donely.ai\/blog\/how-to-create-ai-agent\/"},"modified":"2026-05-16T09:00:08","modified_gmt":"2026-05-16T09:00:08","slug":"how-to-create-ai-agent","status":"publish","type":"post","link":"https:\/\/blog-origin.donely.ai\/blog\/how-to-create-ai-agent\/","title":{"rendered":"How to Create AI Agent: A Step-by-Step Guide"},"content":{"rendered":"<p>AI agents are changing <a href=\"https:\/\/en.wikipedia.org\/wiki\/How\" rel=\"nofollow noopener\" target=\"_blank\">how<\/a> teams get work done. You can spin one up in a day instead of weeks. In this guide you\u2019ll walk through every step you need to define, build, test, and launch an AI <a href=\"https:\/\/en.wikipedia.org\/wiki\/Agent\" rel=\"nofollow noopener\" target=\"_blank\">agent<\/a> that actually helps your business.<\/p>\n<p>We\u2019ll cover purpose definition, framework selection, architecture design, knowledge\u2011base setup, and production deployment. You\u2019ll also see where <a href=\"https:\/\/donely.ai\" rel=\"noopener\" target=\"_blank\">Donely<\/a>\u2019s platform fits in, so you can go from idea to live agent without wrestling with servers.<\/p>\n<nav class=\"table-of-contents\" style=\"background: #fafafa;border: 1px solid #ebebeb;border-radius: 10px;padding: 1em 1.25em;margin: 1.5em 0\">\n<h3>Table of Contents<\/h3>\n<ul>\n<li><a href=\"#step-1-define-the-ai-agents-purpose-and-scope\">Step 1: Define the AI Agent\u2019s Purpose and Scope<\/a><\/li>\n<li><a href=\"#step-2-select-the-right-framework-and-tools\">Step 2: Select the Right Framework and Tools<\/a><\/li>\n<li><a href=\"#step-3-design-the-agents-architecture-and-knowledge-base\">Step 3: Design the Agent\u2019s Architecture and Knowledge Base<\/a><\/li>\n<li><a href=\"#step-4-implement-test-and-deploy-your-ai-agent\">Step 4: Implement, Test, and Deploy Your AI Agent<\/a><\/li>\n<li><a href=\"#faq\">FAQ<\/a><\/li>\n<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\n<\/ul>\n<\/nav>\n<h2 id=\"step-1-define-the-ai-agents-purpose-and-scope\">Step 1: Define the AI Agent\u2019s Purpose and Scope<\/h2>\n<p>Before you write any code, ask yourself what problem the agent will solve. A clear purpose keeps the project focused and avoids feature creep. Start by listing the exact task you want the agent to automate , for example, \u201canswer common support tickets\u201d or \u201cdraft LinkedIn posts for our brand.\u201d<\/p>\n<p>Next, map the workflow. Identify the trigger (a Slack message, an email, a webhook), the data the agent needs (customer record, product catalog), the decision points, and the final action (send a reply, create a ticket, publish a post). Sketch this on a whiteboard or a simple diagram. The more explicit you are now, the less you\u2019ll have to re\u2011engineer later.<\/p>\n<p>Consider the level of autonomy. Some agents only suggest actions and wait for human approval; others act fully on their own. Decide which steps need a human safety net. This decision ties directly to security and compliance , you don\u2019t want a sales bot that can approve refunds without oversight.<\/p>\n<p>When you have a solid purpose statement, turn it into a short \u201crole card.\u201d One sentence that tells the model who it is and what it must achieve. For example: \u201cYou are a friendly support assistant that resolves billing questions within 30 seconds.\u201d Keep it under 50 words so the prompt stays crisp.<\/p>\n<blockquote style=\"border-left: 4px solid #3b82f6;margin: 1.5em 0;padding: 1em 1.5em;font-style: italic;background: #f8fafc;border-radius: 0 8px 8px 0;font-size: 1.1em;color: #1e293b\"><p>&#8220;A well\u2011defined purpose is the single biggest predictor of an AI agent\u2019s success.&#8221;<\/p><\/blockquote>\n<p>Donely\u2019s dashboard lets you store this role card in the agent\u2019s BOOTSTRAP.md file, which the platform reads on every start\u2011up. That way you can edit the purpose later without touching code.<\/p>\n<div class=\"pro-tip\" style=\"background: linear-gradient(135deg, #fffbeb, #fef3c7);border-left: 4px solid #f59e0b;padding: 1em 1.5em;margin: 1.5em 0;border-radius: 0 8px 8px 0\"><strong>Pro Tip:<\/strong> Write the purpose as a question first , \u201cWhat does the user need?\u201d , then flip it into a statement. It helps surface hidden requirements.<\/div>\n<p>Finally, validate the scope with stakeholders. Ask the sales lead, the support manager, or the product owner: \u201cIf the agent fails, what\u2019s the impact?\u201d If the answer is \u201chigh,\u201d tighten the guardrails or add a hand\u2011off step.<\/p>\n<p><img decoding=\"async\" alt=\"AI agent purpose planning diagram\" loading=\"lazy\" src=\"https:\/\/rebelgrowth.s3.us-east-1.amazonaws.com\/blog-images\/batch_66592_0_8c34fe7674f8.png\" \/><\/p>\n<p>When you\u2019ve nailed the purpose, you can move to the tech side with confidence.<\/p>\n<p><a href=\"https:\/\/www.ibm.com\/think\/ai-agents\">IBM\u2019s AI agents guide<\/a> explains how purpose drives the agentic architecture and why clear scope reduces hallucinations.<\/p>\n<p><strong>Bottom line:<\/strong>Define a single, concrete goal, map the trigger\u2011to\u2011action flow, and lock the purpose in a role card before any code is written.<\/p>\n<h2 id=\"step-2-select-the-right-framework-and-tools\">Step 2: Select the Right Framework and Tools<\/h2>\n<p>Choosing a framework is like picking the right toolbox. The right one gives you coordination primitives, state handling, and built\u2011in observability so you don\u2019t rewrite plumbing for every project.<\/p>\n<p>There are several production\u2011grade options in 2026. OpenAI\u2019s Agents SDK offers a simple handoff model that works well if you\u2019re already on the OpenAI stack. LangGraph gives you a graph\u2011based visual workflow with checkpointing, which is handy for complex branching logic. CrewAI focuses on role\u2011based delegation and is great for quick prototypes. Microsoft\u2019s AutoGen shines for iterative code\u2011review loops, while Google\u2019s ADK adds native multimodal support.<\/p>\n<p>To decide, compare three factors:<\/p>\n<ul>\n<li><strong>Orchestration model:<\/strong>Do you need a linear chain, a hierarchy, or a swarm of peers?<\/li>\n<li><strong>State management:<\/strong>Will agents share a mutable state, or should each keep its own snapshot?<\/li>\n<li><strong>Tool integration:<\/strong>Does the framework let you call external APIs, databases, or browser automation easily?<\/li>\n<\/ul>\n<p>For most small\u2011to\u2011mid\u2011size teams, starting with a model\u2011agnostic framework like LangGraph saves future re\u2011writes because you can swap LLM providers later.<\/p>\n<p>Donely\u2019s platform already bundles OpenClaw, which supports all major frameworks under the hood. You can spin up a LangGraph\u2011style graph in the visual builder, then export the JSON to run on any compatible runtime.<\/p>\n<p>Here\u2019s a quick decision matrix you can copy into a spreadsheet:<\/p>\n<table style=\"width: 100%;border-collapse: separate;border-spacing: 0;margin: 2rem 0;border-radius: 12px;overflow: hidden;border: 1px solid #ebebeb\">\n<tr>\n<th style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #e5e5e5;background-color: #fafafa;font-size: 0.78rem;font-weight: 600;color: #6b7280;text-transform: uppercase;letter-spacing: 0.06em\">Framework<\/th>\n<th style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #e5e5e5;background-color: #fafafa;font-size: 0.78rem;font-weight: 600;color: #6b7280;text-transform: uppercase;letter-spacing: 0.06em\">Best for<\/th>\n<th style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #e5e5e5;background-color: #fafafa;font-size: 0.78rem;font-weight: 600;color: #6b7280;text-transform: uppercase;letter-spacing: 0.06em\">Key trade\u2011off<\/th>\n<\/tr>\n<tr>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">OpenAI Agents SDK<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Simple handoff flows<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Locked to OpenAI models<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">LangGraph<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Complex branching with checkpoints<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">More verbose setup<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">CrewAI<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Rapid prototyping<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Lacks deep state persistence<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">AutoGen<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Iterative content generation<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Higher token cost at scale<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Google ADK<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Multimodal &amp; A2A protocol<\/td>\n<td style=\"padding: 0.85rem 1.2rem;text-align: left;vertical-align: middle;border-bottom: 1px solid #ebebeb;color: #1a1a1a;font-size: 0.92rem;line-height: 1.55;background: #fff\">Requires Google Cloud services<\/td>\n<\/tr>\n<\/table>\n<div class=\"stat-highlight\" style=\"text-align: center;padding: 1.5em;margin: 1.5em 0;background: #f0fdf4;border-radius: 12px;border: 1px solid #bbf7d0\"><span class=\"stat-number\" style=\"font-size: 2.5em;font-weight: 800;color: #16a34a;line-height: 1.2\">800+<\/span><span class=\"stat-label\" style=\"font-size: .95em;color: #374151;margin-top: .3em\">integrations in Donely\u2019s catalog<\/span><\/div>\n<p>Having a huge integration catalog matters because you\u2019ll spend less time building custom connectors. Donely lists over 800 native integrations, from CRM to finance tools, so most workflows can be wired with a few clicks.<\/p>\n<p>Watch this short video for a visual walk\u2011through of framework selection:<\/p>\n<p><iframe allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen=\"\" frameborder=\"0\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/eA9Zf2-qYYM\" width=\"560\"><\/iframe><\/p>\n<p>Once you pick a framework, add the right SDKs to your project and configure the model provider. Donely lets you choose OpenAI, Anthropic, or Gemini with a single dropdown, which cuts the setup time to minutes.<\/p>\n<p>Need a concrete example? The <a href=\"https:\/\/donely.ai\/usecases\/business-advisory-council\">Business Advisory Council use case<\/a> shows how a coordinator agent can spawn eight sub\u2011agents in parallel using OpenClaw\u2019s<code>sessions_spawn<\/code>tool. The code lives in a few lines of JSON, yet the pattern scales to any number of specialized agents.<\/p>\n<p>GuruSup&#8217;s multi\u2011agent framework comparison gives a deeper dive on orchestration patterns if you want to explore beyond the basics.<\/p>\n<p><strong>Bottom line:<\/strong>Pick a framework that matches your workflow complexity, supports the tools you need, and fits your team\u2019s skill set.<\/p>\n<h2 id=\"step-3-design-the-agents-architecture-and-knowledge-base\">Step 3: Design the Agent\u2019s Architecture and Knowledge Base<\/h2>\n<p>Architecture is the skeleton that keeps the agent alive when traffic spikes or data changes. Start with a design pattern that matches the problem size. Google Cloud\u2019s guide outlines three common patterns: single\u2011agent, sequential multi\u2011agent, and parallel multi\u2011agent. A single\u2011agent works for simple, one\u2011off tasks. Sequential pipelines are cheap and easy for linear processes. Parallel pipelines shine when you need several specialists to work at the same time.<\/p>\n<p>Map out the components:<\/p>\n<ul>\n<li><strong>Model layer:<\/strong>The LLM that does reasoning. Choose a provider that balances cost and capability for your use case.<\/li>\n<li><strong>Tool layer:<\/strong>APIs, database connections, browser automation, etc. Each tool should have a clear input\u2011output contract.<\/li>\n<li><strong>Orchestrator:<\/strong>Either the framework\u2019s built\u2011in scheduler or a custom state machine that decides which agent runs next.<\/li>\n<li><strong>Knowledge base:<\/strong>Structured data the agent draws from , FAQs, product catalogs, policy documents.<\/li>\n<\/ul>\n<p>When you build the knowledge base, structure it for AI consumption. Instead of long narrative articles, break content into discrete sections with clear headings, bullet lists, and explicit actions. Observe.ai notes that agents struggle with unstructured prose; turning each article into \u201cquestion \u2192 answer\u201d pairs boosts retrieval accuracy dramatically.<\/p>\n<p>For example, a support FAQ should have a heading like \u201cHow do I reset my password?\u201d followed by a concise 2\u2011sentence answer and a JSON\u2011style snippet of the exact steps. Tag each entry with metadata (category, confidence score) so the retrieval layer can rank results.<\/p>\n<p>Donely\u2019s platform lets you connect a Notion database as a live knowledge source. The <a href=\"https:\/\/donely.ai\/usecases\/health-food-tracking-journal\">Health &amp; Food Tracking Journal use case<\/a> demonstrates how a daily\u2011log knowledge base powers a nutrition assistant without any extra code.<\/p>\n<p>Don\u2019t forget context engineering. The agent must know which pieces of knowledge to pull for a given request. Use a simple rule: if the user mentions \u201corder status,\u201d query the orders table; if they mention \u201crefund policy,\u201d hit the policy doc. This keeps the prompt short and reduces token waste.<\/p>\n<div class=\"key-takeaway\" style=\"background: linear-gradient(135deg, #eff6ff, #dbeafe);border-left: 4px solid #2563eb;padding: 1em 1.5em;margin: 1.5em 0;border-radius: 0 8px 8px 0\"><strong>Key Takeaway:<\/strong> Structure knowledge as bite\u2011size, searchable chunks and tie each chunk to a clear trigger.<\/div>\n<p>Security also plays a role in architecture. Apply RBAC (role\u2011based access control) so the agent can only read the data it needs. Donely adds RBAC and audit logs out of the box, which satisfies most compliance checks for SMBs.<\/p>\n<p>Finally, prototype the design on paper. Sketch a diagram that shows the model, the tools, the orchestrator, and the knowledge store. Then validate it with a small \u201chello world\u201d flow before you commit to full implementation.<\/p>\n<p><img decoding=\"async\" alt=\"AI agent architecture schematic\" loading=\"lazy\" src=\"https:\/\/rebelgrowth.s3.us-east-1.amazonaws.com\/blog-images\/batch_66592_1_816beaa76e87.png\" \/><\/p>\n<p><a href=\"https:\/\/docs.cloud.google.com\/architecture\/choose-design-pattern-agentic-ai-system\">Google Cloud\u2019s agentic design patterns<\/a> provide templates you can copy\u2011paste into your own diagrams.<\/p>\n<p><strong>Bottom line:<\/strong>Choose a pattern that fits your workflow, build a modular knowledge base, and lock down access with RBAC before you start coding.<\/p>\n<h2 id=\"step-4-implement-test-and-deploy-your-ai-agent\">Step 4: Implement, Test, and Deploy Your AI Agent<\/h2>\n<p>Implementation is where code meets the design you built. Start by cloning a starter repo from Donely\u2019s OpenClaw templates. The repo includes a pre\u2011filled BOOTSTRAP.md, a sample openclaw.json, and CI scripts that hook into MLflow for evaluation.<\/p>\n<p>Write the prompt logic in the BOOTSTRAP file. Keep it short, use the role card you created earlier, and list the tools the agent can call. For example, add a line like \u201cYou may use the `search_orders` tool to look up a customer\u2019s order ID.\u201d<\/p>\n<p>Next, write thin wrappers for each external tool. Use Donely\u2019s integration plugins , a simple JSON block that maps a tool name to an API endpoint, authentication token, and request schema. This approach avoids hard\u2011coding secrets and lets you swap providers later.<\/p>\n<p>Testing should cover three layers:<\/p>\n<ul>\n<li><strong>Unit tests<\/strong>for each tool wrapper , ensure the HTTP call returns the expected shape.<\/li>\n<li><strong>Agent flow tests<\/strong>that simulate a full conversation. Feed the agent a user request and assert the final action matches the goal.<\/li>\n<li><strong>Evaluation metrics<\/strong>using an agent\u2011focused framework. MLflow\u2019s evaluation suite can score goal\u2011plan\u2011action (GPA) compliance, tool\u2011call correctness, and hallucination rate across multi\u2011turn dialogs.<\/li>\n<\/ul>\n<p>Run the tests locally, then push the code to a Git branch. Donely\u2019s CI\/CD pipeline picks up the change, builds a container, and runs the MLflow test suite automatically. If any metric falls below your threshold, the pipeline blocks the deploy.<\/p>\n<p>When tests pass, click Deploy in the Donely dashboard. The platform provisions a dedicated container, installs the model, attaches the selected integrations, and exposes the agent on the channels you enabled (Slack, WhatsApp, API). Within seconds you get a live URL and health metrics.<\/p>\n<p>After launch, monitor the audit log and the MLflow dashboard. Look for spikes in tool\u2011call failures or unexpected token usage. If you see a pattern, tweak the prompt or add a guardrail in the BOOTSTRAP file.<\/p>\n<p>For a real\u2011world example, the <a href=\"https:\/\/donely.ai\/usecases\/ai-customer-support-agent\">AI Customer Support Agent use case<\/a> shows how a support bot can triage tickets, fetch order data from Stripe, and hand off complex cases to a Slack channel, all while keeping a full audit trail.<\/p>\n<p>Remember to version your agent configuration. Each time you change a prompt or add a new tool, increment the version number in openclaw.json. This makes rollback painless if a new change breaks something.<\/p>\n<div class=\"pro-tip\" style=\"background: linear-gradient(135deg, #fffbeb, #fef3c7);border-left: 4px solid #f59e0b;padding: 1em 1.5em;margin: 1.5em 0;border-radius: 0 8px 8px 0\"><strong>Pro Tip:<\/strong> Enable MLflow\u2019s \u201chuman\u2011in\u2011the\u2011loop\u201d feature to collect real user feedback and automatically fine\u2011tune your LLM judges.<\/div>\n<p><strong>Bottom line:<\/strong>Build with modular prompts, test with unit and flow checks, use MLflow for evaluation, and let Donely handle the one\u2011click deployment.<\/p>\n<h2 id=\"faq\">FAQ<\/h2>\n<h3>What is the first thing I should do before building an AI agent?<\/h3>\n<p>Start with a clear purpose and scope. Write a one\u2011sentence role card that tells the agent who it is and what outcome it must achieve. This step saves time later by keeping the prompt focused and the workflow bounded.<\/p>\n<h3>Which framework is best for a beginner?<\/h3>\n<p>If you are new to agentic AI, CrewAI offers the quickest start\u2011up with under 20 lines of Python. It handles task delegation and tool calls out of the box, so you can prototype a simple workflow before moving to a more feature\u2011rich option like LangGraph.<\/p>\n<h3>How do I choose the right LLM for my agent?<\/h3>\n<p>Match the model to the task. For short, factual answers, a smaller model like Claude\u202fSonnet works well and keeps costs low. For creative drafting or long\u2011form generation, GPT\u20115.4 offers higher quality. Donely lets you switch providers without redeploying the whole container.<\/p>\n<h3>Can I use multiple AI agents at the same time?<\/h3>\n<p>Yes. Donely supports unlimited multi\u2011instance deployment, meaning you can run dozens of agents in parallel, each with its own permissions and knowledge base. Just make sure each instance has the appropriate RBAC settings to keep data isolated.<\/p>\n<h3>What security measures should I add?<\/h3>\n<p>Enable role\u2011based access control (RBAC) so each agent only sees the data it needs. Turn on audit logs in Donely to record every API call and decision. Also, store secrets in a managed vault and never hard\u2011code them in the prompt.<\/p>\n<h3>How do I know if my agent is performing well?<\/h3>\n<p>Use an evaluation framework like MLflow to track metrics such as goal\u2011plan\u2011action compliance, tool\u2011call accuracy, and hallucination rate. Combine those scores with business KPIs , for example, tickets resolved per hour or posts drafted per day , to get a full picture of performance.<\/p>\n<h2 id=\"conclusion\">Conclusion<\/h2>\n<p>Building an AI agent is now a matter of following a clear process: define purpose, pick a framework, design a modular architecture, and deploy with built\u2011in testing. Donely\u2019s platform removes the infrastructure hassle, offers unlimited multi\u2011instance support, and provides RBAC plus audit logs out of the box, which makes scaling from a single bot to an entire AI workforce painless.<\/p>\n<p>If you want a visual, no\u2011code way to spin up agents even faster, check out our No\u2011Code AI Agent Builder guide. It walks you through the same steps with a drag\u2011and\u2011drop canvas, so you can get a production\u2011ready agent up in minutes.<\/p>\n<p>Remember, the secret to a reliable AI agent is a crystal\u2011clear purpose, a well\u2011structured knowledge base, and continuous testing. Follow the steps in this guide, use Donely\u2019s built\u2011in tools, and you\u2019ll have an AI employee that boosts productivity without the usual headaches.<\/p>\n<p><strong>Bottom line:<\/strong>With a solid plan and Donely\u2019s all\u2011in\u2011one platform, you can create, test, and launch AI agents that work reliably at scale.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI agents are changing how teams get work done. You can spin one up in a day instead of weeks. In this guide you\u2019ll walk through every step you need to define, build, test, and launch an AI agent that actually helps your business. We\u2019ll cover purpose definition, framework selection, architecture design, knowledge\u2011base setup, and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":312,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-311","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-agents"],"_links":{"self":[{"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/posts\/311","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/comments?post=311"}],"version-history":[{"count":0,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/posts\/311\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/media\/312"}],"wp:attachment":[{"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/media?parent=311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/categories?post=311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog-origin.donely.ai\/blog\/wp-json\/wp\/v2\/tags?post=311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}