@masterchiefin12:

Master Chiefin

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Friday 14 October 2022 19:48:26 GMT

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Comments

Sean Norton :

Jeffery

2022-10-14 23:37:47

422

gaztn1u :

que bendicion

2022-10-15 02:12:06

895

akira :

eu chorava

2022-10-19 21:09:47

871

♡︎𝕫𝕖𝕝𝕕𝕒♡︎ :

Por eso siempre revisen sus suelos

2022-10-14 21:40:42

228

Kawa tylko biała :

Smacznego🤠👍

2022-10-19 04:11:21

84

ghost :

Fred? que haces con daphne?

2022-10-19 19:32:21

270

❤️مؤخرة كريهة الرائحة :

il bro hai i suoi stessi occhili

2022-10-14 21:17:23

75

luca :

jeffry in azione

2022-10-19 05:07:55

56

Мышь в холодильнике :

шурик

2022-10-20 03:15:10

111

T1muqq🧟‍♂️🦠 :

Джеффри Даммер

2022-10-19 17:07:42

243

rebotmon :

bro get in other level of simping 💀

2022-10-14 19:54:23

147

ruser209816vuser :

Such an angel 😦 @Ingrid

2022-10-15 02:30:17

191

☠︎︎𝔜𝔲𝔯𝔦 ☠︎︎ :

Jeffery??

2022-10-21 17:44:59

135

Rasfort Ranol :

hace rato me apareció el video de la chica😱

2022-10-15 00:21:22

19

😮‍💨 :

On souffle

2022-10-21 21:04:30

5

★ :

a que aún sigue hay

2022-10-25 13:28:53

8

Oreo Box :

next victim

2022-10-14 22:22:14

5

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@NUV Energia #nuvenergia #nuvclipfy #clipfyleague

The Architecture Behind Production-Ready AI Agents 🚀 Building an AI agent isn’t just about connecting an LLM to a prompt. Enterprise-grade agents need reasoning, knowledge, tools, orchestration, security, and observability working together. Here’s a simple 5-layer architecture 👇 1️⃣ MODEL LAYER 🧠 The Brain Provides the intelligence behind the agent. 🔹 LLMs 🔹 Multimodal Models 🔹 Embedding Models 🔹 Reasoning Models Examples: 🤖 GPT 🦙 Llama ✨ Gemini 🔥 Claude 2️⃣ KNOWLEDGE & CONTEXT LAYER 📚 Give the Agent the Right Information Agents need access to relevant knowledge. 🔍 RAG 🧠 Embeddings 🗄️ Vector Databases 📄 Document Stores 🔎 Search & Retrieval Typical flow: Query → Retrieve → Rerank → Context → LLM 3️⃣ TOOL & ACTION LAYER 🛠️ Let the Agent Do Things Instead of only generating text, agents can interact with external systems. 🔌 APIs 🗄️ Databases 🌐 Web Search 📧 Email 📊 Business Systems 💻 Code Execution Think → Choose Tool → Execute → Observe Result 4️⃣ AGENT ORCHESTRATION LAYER 🔄 Coordinate the Workflow This layer controls how agents reason and interact with tools. 🧩 Workflows 🔀 Routing 🧠 Memory 🤝 Multi-Agent Systems 🔁 Planning & Loops ⚡ Human-in-the-Loop Popular ecosystems include: 🔗 LangGraph 🦜 LangChain 🦙 LlamaIndex 🤖 AutoGen 5️⃣ PRODUCTION & GOVERNANCE LAYER 🛡️ Make AI Safe, Reliable & Scalable Enterprise AI needs more than accuracy. 🔐 Authentication & Authorization 📊 Observability 📝 Logging 🧪 Evaluation 🛡️ Guardrails 💰 Cost Monitoring ⚡ Scalability 🔄 Versioning 🏗️ THE 5-LAYER STACK 🧠 Model ⬇️ 📚 Knowledge & Context ⬇️ 🛠️ Tools & Actions ⬇️ 🔄 Agent Orchestration ⬇️ 🛡️ Production & Governance ⸻ 💼 EXAMPLE: ENTERPRISE AI SUPPORT AGENT 👤 Customer asks a question ⬇️ 🧠 LLM understands the request ⬇️ 📚 RAG retrieves company policy ⬇️ 🛠️ Agent checks the CRM ⬇️ 🔄 Orchestrator decides the next action ⬇️ 🛡️ Guardrails & evaluation validate the response ⬇️ 💬 Customer receives an answer 💡 KEY TAKEAWAY A production AI agent is more than an LLM with tools. The real stack combines: Intelligence + Knowledge + Actions + Orchestration + Governance That’s the difference between a cool AI demo and an enterprise-ready AI system. 🚀 #AgenticAI #AIAgents #GenerativeAI #creatorsearchinsights #datascience

The Architecture Behind Production-Ready AI Agents 🚀 Building an AI agent isn’t just about connecting an LLM to a prompt. Enterprise-grade agents need reasoning, knowledge, tools, orchestration, security, and observability working together. Here’s a simple 5-layer architecture 👇 1️⃣ MODEL LAYER 🧠 The Brain Provides the intelligence behind the agent. 🔹 LLMs 🔹 Multimodal Models 🔹 Embedding Models 🔹 Reasoning Models Examples: 🤖 GPT 🦙 Llama ✨ Gemini 🔥 Claude 2️⃣ KNOWLEDGE & CONTEXT LAYER 📚 Give the Agent the Right Information Agents need access to relevant knowledge. 🔍 RAG 🧠 Embeddings 🗄️ Vector Databases 📄 Document Stores 🔎 Search & Retrieval Typical flow: Query → Retrieve → Rerank → Context → LLM 3️⃣ TOOL & ACTION LAYER 🛠️ Let the Agent Do Things Instead of only generating text, agents can interact with external systems. 🔌 APIs 🗄️ Databases 🌐 Web Search 📧 Email 📊 Business Systems 💻 Code Execution Think → Choose Tool → Execute → Observe Result 4️⃣ AGENT ORCHESTRATION LAYER 🔄 Coordinate the Workflow This layer controls how agents reason and interact with tools. 🧩 Workflows 🔀 Routing 🧠 Memory 🤝 Multi-Agent Systems 🔁 Planning & Loops ⚡ Human-in-the-Loop Popular ecosystems include: 🔗 LangGraph 🦜 LangChain 🦙 LlamaIndex 🤖 AutoGen 5️⃣ PRODUCTION & GOVERNANCE LAYER 🛡️ Make AI Safe, Reliable & Scalable Enterprise AI needs more than accuracy. 🔐 Authentication & Authorization 📊 Observability 📝 Logging 🧪 Evaluation 🛡️ Guardrails 💰 Cost Monitoring ⚡ Scalability 🔄 Versioning 🏗️ THE 5-LAYER STACK 🧠 Model ⬇️ 📚 Knowledge & Context ⬇️ 🛠️ Tools & Actions ⬇️ 🔄 Agent Orchestration ⬇️ 🛡️ Production & Governance ⸻ 💼 EXAMPLE: ENTERPRISE AI SUPPORT AGENT 👤 Customer asks a question ⬇️ 🧠 LLM understands the request ⬇️ 📚 RAG retrieves company policy ⬇️ 🛠️ Agent checks the CRM ⬇️ 🔄 Orchestrator decides the next action ⬇️ 🛡️ Guardrails & evaluation validate the response ⬇️ 💬 Customer receives an answer 💡 KEY TAKEAWAY A production AI agent is more than an LLM with tools. The real stack combines: Intelligence + Knowledge + Actions + Orchestration + Governance That’s the difference between a cool AI demo and an enterprise-ready AI system. 🚀 #AgenticAI #AIAgents #GenerativeAI #creatorsearchinsights #datascience

que rico 🥰🤤🤤

que rico 🥰🤤🤤

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