Apr 27
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What is an autonomous AI agent, and how do you build one from a raw LLM? In this video, we explain how autonomous agents evolve from a basic large language model into full multi-agent systems. You’ll learn the core architecture behind agentic AI, including perception, reasoning, planning, action, memory, reflection, tool use, API integration, live database access, and orchestration. We start with the foundations of an AI agent and then walk through the key system design patterns used in modern autonomous AI: raw LLM to agent architecture thought, action, and observation loops tool-using AI agents memory and context window management reflection and critic patterns planning modules and reasoning engines orchestrator-based multi-agent systems research, execution, and review agent roles performance limits of single-agent designs why specialized multi-agent ecosystems scale better This video is useful for anyone working on AI agents, agentic workflows, LLM applications, autonomous systems, AI automation, or multi-agent orchestration. If you’re interested in: autonomous AI agents, agentic AI, multi-agent systems, LLM agents, AI orchestration, planning agents, reflection in AI, memory systems for agents, or building real-world AI workflows, this video is for you. #AutonomousAgents #AgenticAI #MultiAgentSystems #LLMAgents #AIEngineering