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@farhan.sh460: #foryoupage
Farhan Sh
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Region: IR
Sunday 25 October 2026 19:28:09 GMT
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𝑨𝒓𝒕𝒆𝒎𝒊𝒔 :
2026-10-25 20:27:11
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Everyone's building AI agents. Few can explain what's inside one. Here are 9 AI agent concepts in one 8-second loop. 🤖 The model is the easy part. Everything around it decides whether your agent works. 🧠 1. Memory & State: memory recalls the past; state tracks where the task stands right now. 🎛️ 2. Orchestration: decides who does what, and in what order. 📚 3. RAG (retrieval-augmented generation): fetch the right docs, inject them into the prompt, give a grounded answer. 🔁 4. Harness: the loop that lets a model act: context → action → persist → control, repeated. 📏 5. Evals: score outputs against what you expected, then improve. Without them you're shipping on vibes. 🔌 6. MCP (Model Context Protocol): one open standard for connecting agents to tools and data. 📄 7. Skills: reusable instruction files (SKILL.md) an agent loads only when it needs them. 🤝 8. A2A (Agent2Agent protocol): an agent reads another agent's "agent card" to discover what it can do, then hands it tasks. 👥 9. Multi-agent systems: specialist agents sharing one task. 👀 What people miss: when an agent fails, it's usually not the model. It's a missing eval, a leaky harness, or the wrong context. Upgrading to a smarter model rarely fixes a broken loop. Better models raise the ceiling. Concepts 1–9 raise the floor. 📸 Screenshot this before your next AI engineering or system design interview. These 9 come up constantly. Follow @hackproduct for AI engineering concepts you can actually picture. ⚡ . . #AIagents #agenticAI #AIengineering #LLM #GenAI
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