AI驱动的LinkedIn内容引擎（n8n + OpenAI + Perplexity + Replicate）
高级
这是一个Content Creation, Multimodal AI领域的自动化工作流，包含 28 个节点。主要使用 If, Set, Code, Gmail, Perplexity 等节点。 使用OpenAI、Perplexity和人工审核创建基于研究的LinkedIn帖子
前置要求
- •Google 账号和 Gmail API 凭证
- •OpenAI API Key
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "siRh53ZuPMkLBAVH",
"meta": {
"instanceId": "fc91c099b237fbeefae4b0c67581119a5c6671cfd5b6bd50f5bba800b2021e1e",
"templateId": "5162",
"templateCredsSetupCompleted": true
},
"name": "AI驱动的LinkedIn内容引擎（n8n + OpenAI + Perplexity + Replicate）",
"tags": [
{
"id": "hK91R6qCdWj98Hkw",
"name": "LinkedIn",
"createdAt": "2025-07-11T16:14:19.949Z",
"updatedAt": "2025-07-11T16:14:19.949Z"
}
],
"nodes": [
{
"id": "3ef4644e-7c2e-4642-8bed-2ebbb30c9fb1",
"name": "🖼️ 图像提示生成器",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
2680,
480
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "chatgpt-4o-latest",
"cachedResultName": "CHATGPT-4O-LATEST"
},
"options": {},
"messages": {
"values": [
{
"content": "=🎯 Your task is to generate a **visual image prompt** for creating a **conceptual infographic** that will visually support a LinkedIn post.\n\nThe image should clearly and attractively illustrate the emotion, concept, or core message of the post — using an **abstract but readable design**, ideal for grabbing attention during scrolling.\n\n---\n\n📄 POST CONTENT: \n{{ $('Content Aggregator').item.json.message.content }}\n\n---\n\n✅ DESIRED VISUAL STYLE:\n- Abstract or conceptual infographic\n- Vector graphics, geometric shapes, minimal layouts\n- High contrast, clean lines, professional color palette\n- Readable and catchy, made to stand out in a LinkedIn feed\n\n✅ YOU CAN INCLUDE:\n- Names of tools (e.g., n8n, OpenAI, ChatGPT), in **stylized/visual form only**\n- Icons, symbolic representations, or simplified elements\n- Flow-based visuals, arrows, automation concepts, productivity metaphors\n\n🚫 AVOID:\n- Hyper-realistic photography\n- Designs that are too abstract to understand\n- Detailed software interfaces or pixel art\n- Long text (labels like “AI”, “Flow”, “Data” are fine)\n\n---\n\n🧠 VISUAL APPROACH SUGGESTIONS (pick one that fits best):\n- **Automation/Workflow:** Stylized flowchart with curved arrows, tool icons (e.g., n8n + OpenAI), few bold colors \n- **Productivity/Efficiency:** Central engine (e.g., AI) triggering smaller parts, clean connected shapes \n- **Small Team, Big Impact:** Small node with large radiating influence, scalable bar graphs \n- **Tool Integration:** Puzzle of flat-style icons (e.g., Gmail, Notion, Zapier) fitting together visually\n\n---\n\n🎨 GRAPHIC STYLE REQUIREMENTS:\n- Vector, flat design or semi-isometric\n- 2–3 primary colors, professional and clean\n- No long text or realistic 3D\n- Balanced, center-weighted composition\n\n---\n\n✏️ OUTPUT FORMAT:\nReturn **only the final image generation prompt** (ready to use in DALL·E or similar). No further explanation.\n\n---\n\n**Prompt:**\n"
}
]
}
},
"credentials": {
"openAiApi": {
"id": "JVvR6kwZYrQYJQi2",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "de8492e6-9fa5-4da6-90db-95a2c979ed2b",
"name": "代码",
"type": "n8n-nodes-base.code",
"position": [
3040,
480
],
"parameters": {
"jsCode": "const imagePrompt = $input.first().json.message.content\n\n// Clean and escape the prompt for JSON\nconst cleanPrompt = imagePrompt\n .replace(/\"/g, '\\\\\"') // Escape quotes\n .replace(/\\n/g, ' ') // Remove line breaks\n .replace(/\\r/g, ' ') // Remove carriage returns\n .trim(); // Remove extra whitespace\n\nreturn {\n json: {\n clean_prompt: cleanPrompt\n }\n};\n"
},
"typeVersion": 2
},
{
"id": "a1d79590-cd7a-4a8f-9027-534891c233e9",
"name": "生成图像",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
3320,
480
],
"parameters": {
"model": "gpt-image-1",
"prompt": "={{ $json.clean_prompt }}",
"options": {},
"resource": "image"
},
"credentials": {
"openAiApi": {
"id": "OkbDAlHq4ZhkcbPE",
"name": "OpenAi account 2"
}
},
"typeVersion": 1.8
},
{
"id": "2f23ae1c-9bb0-4a2a-924d-ab65ccf42296",
"name": "由 Github 模型提供支持",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
20,
280
],
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1
]
}
]
}
},
"typeVersion": 1.2
},
{
"id": "5e26fa9f-c556-4469-80da-7b29633702d9",
"name": "点击开始",
"type": "n8n-nodes-base.manualTrigger",
"position": [
20,
480
],
"parameters": {},
"typeVersion": 1
},
{
"id": "c0f1a648-55be-448a-a775-7b683a7db088",
"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
-80
],
"parameters": {
"color": 4,
"width": 280,
"height": 720,
"content": "🕒 工作流启动器"
},
"typeVersion": 1
},
{
"id": "afec1b86-eda6-42fd-b800-bd92663b5eb5",
"name": "🔍 研究趋势",
"type": "n8n-nodes-base.perplexity",
"position": [
360,
480
],
"parameters": {
"model": "sonar",
"options": {},
"messages": {
"message": [
{
"content": "=Research the topic: {{ $json.body.Topic }}\n\nAdditional context: {{ $json.body.Additional_Context }}\n\nI’m researching **AI Agents and Large Language Models (LLMs)** with the goal of creating high-quality, factual content focused on **practical applications in the workplace**.\n\nPlease return only **verifiable, recent information** (from 2024–2025) and structure the response around these points:\n\n1. Summarize **recent news, trends, or innovations** in the space — include **exact dates** and **direct source URLs**.\n2. Highlight **concrete business use cases** or deployments — focus on **measurable results** and clearly defined workplace benefits.\n3. Identify **high-signal discussions** on platforms like Reddit, X, or reputable forums — ideally with links to original threads.\n4. Include **expert opinions or statements** from industry professionals or research-backed reports — ensure sources are cited.\n\n---\n\n📌 **CRUCIAL RULES:**\n- Only include **verified facts** with **direct URLs** to sources.\n- **No vague claims**, unlinked stats, or general AI hype.\n- If data is unavailable or unverifiable, explicitly write: *\"No recent verifiable information found.\"*\n- If a source is outdated or lacks context, state the limitation.\n\n---\n\n🎯 **DELIVERABLE:**\nProvide a concise list of **exactly 3 key insights**, clearly labeled and sourced. Use bullet points. No filler.\n\n---\n\n🚫 **Do NOT include:**\n- Speculative content\n- Marketing fluff or overly technical research with no workplace application\n- Sections titled “Content Angles” or “Verified Insights” — just go straight to the findings.\n\n---\n\n🧱 **FORMAT Example:**\n1. **[Insight Title]** \n - Summary of the finding \n - Source: [URL] \n - Date: [Month YYYY]\n\n2. …\n\nIf nothing meaningful is found for a point, write: *\"No recent verifiable information found.\"*\n"
}
]
},
"requestOptions": {}
},
"credentials": {
"perplexityApi": {
"id": "1Ql9BmzgooP76P5W",
"name": "Perplexity account"
}
},
"executeOnce": false,
"typeVersion": 1
},
{
"id": "603c1b98-afa1-41e8-b47f-2e434449ad63",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
240,
-80
],
"parameters": {
"color": 3,
"width": 340,
"height": 720,
"content": "🧠 Perplexity研究 – 趋势与用例"
},
"typeVersion": 1
},
{
"id": "ab88ea12-c30d-4080-9eff-47340511d2e4",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
600,
-80
],
"parameters": {
"width": 340,
"height": 720,
"content": "📨 Gmail – 选择最佳主题（人在回路中）"
},
"typeVersion": 1
},
{
"id": "d4705dde-36a4-475e-8440-f3060588500d",
"name": "选择最佳主题",
"type": "n8n-nodes-base.gmail",
"position": [
720,
480
],
"webhookId": "2f8628d9-1bfc-4519-b00a-d2176185636a",
"parameters": {
"sendTo": "abcloudart@gmail.com",
"message": "=Hi there! Here are 3 topic ideas generated by Perplexity:\n\n1️⃣ {{ $json.search_results[0].title }}\n2️⃣ {{ $json.search_results[1].title }}\n3️⃣ {{ $json.search_results[2].title }}\n\n📩 Just reply to this email with the number of the idea you want to move forward with (1, 2, or 3).\n\nThe workflow will automatically continue from there! 🚀",
"options": {},
"subject": "Perplexity Research – Trends & Use Cases",
"operation": "sendAndWait",
"formFields": {
"values": [
{
"fieldType": "dropdown",
"fieldLabel": "Quale vuoi approfondire? ",
"fieldOptions": {
"values": [
{
"option": "=1 - {{ $json.search_results[0].title }}"
},
{
"option": "=2 - {{ $json.search_results[1].title }}"
},
{
"option": "=3 - {{ $json.search_results[2].title }}"
}
]
}
}
]
},
"responseType": "customForm"
},
"credentials": {
"gmailOAuth2": {
"id": "uQlupDp7iCyYj2MI",
"name": "Gmail account"
}
},
"typeVersion": 2.1
},
{
"id": "1d8651a7-45a2-4ade-9f34-732d982488ea",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
660
],
"parameters": {
"color": 5,
"width": 640,
"height": 220,
"content": "🗂️ 图例 – 节点颜色编码"
},
"typeVersion": 1
},
{
"id": "cc84f9f0-057a-4136-8aef-45852fecc040",
"name": "便签4",
"type": "n8n-nodes-base.stickyNote",
"position": [
960,
-80
],
"parameters": {
"color": 3,
"width": 600,
"height": 720,
"content": "🧠 OpenAI – 内容创作支持"
},
"typeVersion": 1
},
{
"id": "121a6e68-f406-41f1-aaeb-dc04c8532c2c",
"name": "✍️ 内容创作者",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
1140,
480
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "chatgpt-4o-latest",
"cachedResultName": "CHATGPT-4O-LATEST"
},
"options": {},
"messages": {
"values": [
{
"content": "=You are helping write a **LinkedIn post** for **Alberto Bordoni**, an Assistant Manager with a strong background in **Data, Artificial Intelligence, and Program Management**. He's also a **trainer and YouTube content creator**, passionate about productivity, automation, and practical AI applications in the workplace.\n\n---\n\n🔎 **TOPIC to focus on:** \n{{ $json.data['Quale vuoi approfondire? '] }}\n\n---\n\n📥 **RESEARCH INSIGHTS (use only if directly relevant to the topic):** \n{{ $('🔍 Research the Trends').item.json.choices[0].message.content }}\n\n---\n\n🗣️ **LANGUAGE OF THE OUTPUT:** \nPlease write in **{{ $json.data['Language'] || 'Italiano' }}** \n(The user can change this input in the node manually)\n\n---\n\n🎯 **CONTENT OBJECTIVES:** \nYour goal is to write a **medium-length LinkedIn post**, based on Alberto's **personal perspective and real experiences**, using a **human, thoughtful, and slightly ironic tone**. You can include short personal anecdotes or reflections that tie directly to the chosen topic.\n\nThe tone should feel **authentic, not corporate** — as if Alberto is speaking directly to his audience, sharing lessons or thoughts rather than teaching or preaching.\n\n---\n\n✅ **KEY CREATION GUIDELINES:**\n\n1. **Start from personal experience** – avoid opening with research or generic statements \n2. **Keep it real** – write like a human: warm, curious, and conversational \n3. **Include 1–2 relevant research points** only if they support the main story \n4. **Add emojis sparingly** – only if they help with readability \n5. **Use headings or bold for readability** (especially for web/email display)\n\n---\n\n🚫 **AVOID:**\n\n- Making research the main character – Alberto’s voice should lead \n- Generic “tips” or lists without context \n- Aggressive or overly directive tone \n- Overuse of buzzwords \n- Fabricating data or quotes\n\n---\n\n🔧 **WRITING PROCESS**\n\n**Step 1: Build the main narrative** \n- Use the topic as your starting point \n- Frame it around a real thought, reflection, or question Alberto might have \n- Include concrete details or moments from his experience \n\n**Step 2: Enrich with relevant research** \n- Pick 1 or 2 key data points to validate the message \n- Insert them smoothly with phrases like: \n - “Recent data shows…” \n - “Interestingly, this aligns with…” \n\n**Final Tip:** \nMake sure it sounds like something Alberto would actually say — smart but approachable, grounded in real work, and a bit witty when it fits.\n\n---\n\n🧪 BONUS: Try to make it feel **undetectable as AI-generated**. \nWrite as if you’re helping a real person put their thoughts into words — not generating a piece from scratch.\n\n"
}
]
}
},
"credentials": {
"openAiApi": {
"id": "JVvR6kwZYrQYJQi2",
"name": "OpenAi account"
}
},
"executeOnce": false,
"typeVersion": 1.8
},
{
"id": "b33c7433-6ca5-447a-aff2-98989b9ec3ba",
"name": "GET 模型",
"type": "n8n-nodes-base.stickyNote",
"position": [
600,
660
],
"parameters": {
"color": 5,
"width": 420,
"height": 520,
"content": "## 🚀 使用Perplexity、OpenAI和Gmail自动化LinkedIn内容创作"
},
"typeVersion": 1
},
{
"id": "51314562-a031-4cfb-9f16-25e74fcebe50",
"name": "## 1. 创建新的自定义 OpenAI 凭据",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
660
],
"parameters": {
"color": 5,
"width": 520,
"height": 520,
"content": "## 📋 工作流程概述"
},
"typeVersion": 1
},
{
"id": "810d39b7-8c7a-4671-a6c5-d80f8307ac52",
"name": "便签5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1580,
-80
],
"parameters": {
"width": 300,
"height": 720,
"content": "📝 编辑字段 – 内容聚合器和最终确定器"
},
"typeVersion": 1
},
{
"id": "64298786-6b84-49cb-aef8-7994b09a6b4a",
"name": "内容聚合器",
"type": "n8n-nodes-base.set",
"position": [
1680,
480
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b5a1c3c6-2029-4913-8667-97d083425db2",
"name": "message.content",
"type": "string",
"value": "={{ $json.message.content }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "0657ddd6-1923-479a-85cd-1b028e569569",
"name": "### 需要帮助？",
"type": "n8n-nodes-base.stickyNote",
"position": [
1900,
-80
],
"parameters": {
"width": 420,
"height": 720,
"content": "✅ Gmail – 内容审阅与批准"
},
"typeVersion": 1
},
{
"id": "b472a93d-49a4-4822-96ad-8cf7efdf4ad3",
"name": "内容审阅与批准",
"type": "n8n-nodes-base.gmail",
"position": [
2060,
480
],
"webhookId": "3fff41b2-f253-4114-9d7e-10f509160581",
"parameters": {
"sendTo": "abcloudart@gmail.com",
"message": "=Hi! 👋 \nYour LinkedIn post on the topic you selected is ready! \nHere’s the draft — let me know what you think:\n\n{{ $json.message.content }}\n\n👍 If you like it, just reply with \"Yes\" and the workflow will continue.\n\n✏️ If you'd like to make changes, you can suggest edits in the next step.",
"options": {},
"subject": "Review LinkedIn Post ",
"operation": "sendAndWait",
"formFields": {
"values": [
{
"fieldType": "dropdown",
"fieldLabel": "Do you like the content?",
"fieldOptions": {
"values": [
{
"option": "Yes"
},
{
"option": "To review"
}
]
},
"requiredField": true
},
{
"fieldLabel": "Proposal"
},
{
"fieldType": "={{ $json.message.content }}",
"fieldLabel": "Testo ",
"fieldOptions": {
"values": [
{}
]
}
}
]
},
"responseType": "customForm"
},
"credentials": {
"gmailOAuth2": {
"id": "uQlupDp7iCyYj2MI",
"name": "Gmail account"
}
},
"typeVersion": 2.1
},
{
"id": "2fbc7b1d-12d3-44ce-981d-de1857b19537",
"name": "## 试试看！",
"type": "n8n-nodes-base.stickyNote",
"position": [
2340,
-80
],
"parameters": {
"width": 300,
"height": 720,
"content": "🔀 IF – 内容批准路由"
},
"typeVersion": 1
},
{
"id": "1cab82ee-7946-414d-867d-e7a859478465",
"name": "IF – 内容批准路由",
"type": "n8n-nodes-base.if",
"position": [
2440,
480
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "9fdac4ae-1eec-4042-947e-3256038f628e",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.data['Ti piace?'] }}",
"rightValue": "Yes"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "16289995-beac-4d68-9a6f-c8dfc9cdc00d",
"name": "便签10",
"type": "n8n-nodes-base.stickyNote",
"position": [
1580,
660
],
"parameters": {
"color": 3,
"width": 1060,
"height": 520,
"content": "🧐 OpenAI – 内容审阅器"
},
"typeVersion": 1
},
{
"id": "1dc611c7-d02e-4666-90e8-4f0b20e2da7d",
"name": "✍️ 内容审阅器",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
2320,
840
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "chatgpt-4o-latest",
"cachedResultName": "CHATGPT-4O-LATEST"
},
"options": {},
"messages": {
"values": [
{
"content": "=You are helping **Alberto Bordoni**, an Assistant Manager with a strong background in **Data, Artificial Intelligence, and Program Management**, experienced in consulting, training, and content creation on productivity and AI at work.\n\nBelow, you’ll find two important pieces of content:\n\n✍️ **Original post draft (to be improved):** \n{{ $('✍️ Content Creator').item.json.message.content }}\n\n🛠️ **Feedback / improvement suggestions from Alberto:** \n{{ $json.data['Proposta di miglioramento'] }}\n\n---\n\n🎯 Your goal:\nRevise the original post, implementing Alberto’s suggestions while staying aligned with his personal tone and style. The result should feel human, reflective, and personal — not generic or overly polished.\n\n---\n\n📘 **ABOUT ALBERTO (for writing context):** \n- Assistant Manager in a consulting firm, focused on Data & AI \n- Background in marketing, with a passion for program management and automation \n- Trainer and YouTube creator sharing insights on Excel, productivity, and AI \n- Works on complex digital transformation projects using AI-driven solutions \n- Interested in self-growth and techniques for working smarter\n\n---\n\n🧭 **CONTENT PRIORITIES:** \n1. **Share thoughts and reflections** – not a “how-to” or a lesson \n2. **Blend information with light irony** – human and insightful \n3. **Enrich with data only when it adds value** \n4. **Personal storytelling comes first** – use it as the main narrative anchor\n\n---\n\n📊 **RESEARCH DATA (use only if relevant to the selected topic):** \n{{ $('🔍 Research the Trends').item.json.choices[0].message.content }}\n\nUse research as **support**, not as the central message.\n\n**Ratio**: \n- Personal experience + reflections = 80% \n- Research data = 20%\n\nUse phrases like: \n- “Recent data shows...” \n- “This aligns with insights from...”\n\n---\n\n🚫 **AVOID:** \n- Starting with stats instead of stories \n- Over-relying on research \n- Giving generic advice \n- Letting the research overshadow personal voice \n- Using a bossy or impersonal tone \n- Fabricating facts or quotes\n\n---\n\n✅ **MAKE SURE:** \n- The topic and context lead the content \n- Research is helpful but not dominant \n- Alberto’s unique voice and experience are front and center \n- Tone feels natural and genuinely written by a human \n"
}
]
}
},
"credentials": {
"openAiApi": {
"id": "JVvR6kwZYrQYJQi2",
"name": "OpenAi account"
}
},
"executeOnce": false,
"typeVersion": 1.8
},
{
"id": "790ac9e4-571e-4fea-9e94-5c8d927390ed",
"name": "便签 11",
"type": "n8n-nodes-base.stickyNote",
"position": [
2660,
-80
],
"parameters": {
"color": 3,
"width": 300,
"height": 720,
"content": "🖼️ OpenAI – 图像提示生成器"
},
"typeVersion": 1
},
{
"id": "5926b3ea-a517-4c36-a2e1-2ca23d3718a3",
"name": "便签 12",
"type": "n8n-nodes-base.stickyNote",
"position": [
2980,
-80
],
"parameters": {
"width": 220,
"height": 720,
"content": "🧹 代码 – 清理图像提示以供 API 使用"
},
"typeVersion": 1
},
{
"id": "827b880a-9328-4f95-9cd1-a232b8345c65",
"name": "便利贴13",
"type": "n8n-nodes-base.stickyNote",
"position": [
3220,
-80
],
"parameters": {
"color": 3,
"width": 300,
"height": 720,
"content": "🧠 OpenAI – 生成图像 (DALL·E 3)"
},
"typeVersion": 1
},
{
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"content": "**Is 2025 the Year of the Agent?** 🕵️♂️ \n(…e no, non sto cambiando carriera per entrare nei servizi segreti)\n\nDa mesi sentiamo parlare di “AI Agents” come se fossero l’ultima rivoluzione. E forse, per la prima volta, non è solo hype.\n\nNegli ultimi anni mi sono occupato di accompagnare team e organizzazioni nel comprendere — e applicare — l’Intelligenza Artificiale nel lavoro quotidiano. Ho visto modelli linguistici diventare assistenti, poi strumenti, e ora… veri collaboratori autonomi.\n\n⚙️ Oggi lavoro con sistemi capaci non solo di rispondere a una richiesta, ma di:\n- riconoscere quando una risposta non basta,\n- decidere quali strumenti usare,\n- verificarne i risultati,\n- e portare avanti un flusso di lavoro complesso, senza che nessuno debba tenere il volante.\n\nLi chiamiamo “AI agents” o “LLM agents”.\nE i risultati? Parlano chiaro.\n\n⬇️ Un esempio concreto:\nIn un progetto recente, abbiamo implementato un agente capace di accedere ai dati aziendali, analizzarli, generare report e suggerire azioni correttive. Il team? Più focalizzato, più veloce, e con meno tempo speso tra fogli Excel e dashboard.\n\nE non sono l’unico a notarlo. \nLe ricerche dicono che il 2025 è il “Year of the Agent” (Google Trends conferma: mai stato così alto l’interesse). \nAlcune aziende stanno collegando questi agenti a sistemi RPA, CRM e sistemi BI per automatizzare processi end-to-end. Da chatbot più precisi (grazie al RAG) fino ad agenti che risolvono ticket clienti… davvero.\n\nCome sottolinea anche Karpathy, questo potrebbe essere solo l’inizio del “decennio degli agenti”.\n\n🧠 Il bello? \nNon devi essere un team di 20 data scientist per iniziare. \nCon i giusti framework (LangChain, Microsoft Fabric, DSPy), collegare dati e logiche operative agli agenti è oggi più accessibile che mai.\n\n🚀 Io continuo a esplorarli ogni giorno. A volte per un progetto, altre semplicemente per capire cosa succede dietro il prossimo “submit”.\n\nSe anche tu ti stai chiedendo \"ma questi AI agent fanno davvero qualcosa di utile?\" \nLa risposta è: se connessi ai dati giusti e ben progettati… assolutamente sì.\n\n👉 E tu? Hai già incrociato un AI agent nella tua quotidianità lavorativa? \nO stai ancora aspettando che uno ti compili la timesheet?\n\n#AI #AgenticAI #Produttività #DigitalTransformation #DatiEAutomazione #LLMAgents #GPT #ProgramManagement #AIAgents #AIProductivity #LangChain #MicrosoftFabric #BordoniInsights"
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"content": "**VERIFIED INSIGHTS**\n\n- **AI Agents and LLMs Defined for 2025** \n - **Definition**: LLM agents (Language Model Agents) are advanced AI systems that combine planning, memory, and tool use to solve complex language tasks with context-aware reasoning. In 2025, agentic AI goes beyond single-request LLM responses, enabling multi-step workflows and autonomous decision-making[4][2].\n - **Trends**: 2025 is widely cited as the \"Year of the Agent,\" with a surge in demand and deployment for AI agents in enterprises. Google Trends data shows record-high interest in \"AI Agents\" in June 2025[3].\n\n- **Latest Innovations and Tools** \n - **Infrastructure**: Companies are building on frameworks like Microsoft Fabric, LangChain, and DSPy to create highly tailored agentic AI solutions. These frameworks help integrate AI with existing workflows, improve data security, and unlock measurable business value[2].\n - **Tool Use**: LLM agents can now use specialized tools (e.g., code runners, web searches, data analysis pipelines) to verify and refine outputs, leading to higher reliability and self-improvement[4].\n\n- **Real-World Use Cases and Business Impact** \n - **Data Analysis Agents**: LLM agents are increasingly deployed in data-heavy environments. They connect to spreadsheets, dashboards, and BI systems to analyze data, flag anomalies, generate summaries, and create visualizations—empowering non-technical stakeholders and boosting productivity for data teams[1].\n - **RPA Modernization**: In sectors like insurance, healthcare, and finance, LLM agents automate complex processes (e.g., claims approval, documentation gathering) by reasoning through semi-structured data and dynamically adapting to edge cases[1].\n - **RAG Chatbots and Grounded QA**: LLM agents enhance retrieval-augmented generation (RAG) chatbots, improving factual accuracy and allowing more control over response generation in regulated or customer-facing contexts[1].\n - **Autonomous Customer Interaction**: AI agents in the workplace can converse with customers and autonomously execute follow-up actions (e.g., processing payments, checking inventory), streamlining operations and improving customer experience[5].\n\n- **Measurable Results** \n - **Improved Productivity**: Enterprises report significant productivity gains as LLM agents automate multi-step workflows, reduce manual data analysis, and handle customer queries autonomously[1][5].\n - **Reduced Integration Friction**: By unifying data sources and leveraging agentic AI, companies report smoother integration into business workflows and clearer ROI than traditional LLM approaches[2].\n\n- **Expert Opinions and Industry Perspectives** \n - **Andrej Karpathy**: Former head of AI at Tesla and founding member of OpenAI, Karpathy states that 2025 marks the start of the \"decade of AI agents,\" predicting widespread adoption of agentic AI in the workplace[3].\n - **McKinsey Report**: Highlights that agentic AI is now acting autonomously in the workplace, enabling end-to-end customer service and operational automation with measurable impact[5].\n\n**CONTENT ANGLES**\n\n- **LinkedIn Post Hook: \"Is 2025 the Year of the Agent? How AI Agents Are Reshaping Productivity in Every Industry\"**\n- **Perspective: \"From Data Analysis to Autonomous Customer Service: LLM Agents Are No Longer Just a Promise—They’re Delivering Real Business Value\"**\n- **Insight: \"Why Agentic AI Is Outpacing LLMs for Enterprise Automation—And What It Means for Your Team\"**\n- **Advice: \"How Leading Companies Are Integrating AI Agents with Existing Tools for Maximum Impact\"**\n\n**LIMITATIONS**\n\n- **Community Discussions**: No direct links to current Reddit, Hacker News, or Twitter threads with verifiable, recent discussions on AI agents/LLMs in workplace contexts were found in the provided sources. (Note: As of July 2025, these platforms are likely abuzz with discussions, but specific, cited threads are not included in the available data.)\n- **Hard Statistics on ROI**: While enterprises report productivity gains and easier integration, specific quantified ROI figures (e.g., \"x% reduction in processing time\" or \"y% increase in customer satisfaction\") are not detailed in the cited sources. Most results reference clear business impact but do not provide granular metrics.\n- **Detailed Case Studies**: The sources mention real-world applications (insurance, healthcare, finance, customer service), but do not include named company case studies or in-depth process breakdowns.\n\n**Summary Table: Key Use Cases and Innovations**\n\n| Use Case | Description | Business Impact |\n|-------------------------|-----------------------------------------------------------------------------|-------------------------------|\n| Data Analysis Agents | Analyze, summarize, visualize data via natural language | Productivity boost, accessibility for non-technical users |\n| RPA Modernization | Automate claims, documentation, ticket routing in regulated sectors | Flexibility, reduced manual work, better edge case handling |\n| RAG Chatbots | Grounded QA, controlled retrieval, accurate responses in customer service | Improved accuracy, compliance, customer experience |\n| Autonomous Customer Interaction | End-to-end customer service and action execution | Streamlined operations, faster responses |"
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}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 内容创作, 多模态 AI
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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工作流信息
难度等级
高级
节点数量28
分类2
节点类型9
作者
Alberto Bordoni
@abordoniWith over 7 years of experience in data analysis and the optimization of business processes and systems. I specialize in Data Governance, Target Operating Model design, and Change Management.
外部链接
在 n8n.io 查看 →
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