X @Avi Chawla

Core Concepts - LLMs like GPT and DeepSeek serve as the foundational engine powering Agentic AI [1] - AI Agents wrap around LLMs, granting them autonomous action capabilities and making them useful in real-world workflows [2] - Agentic systems emerge from combining multiple agents, enabling collaboration and coordination [3] Agentic Infrastructure - Agentic Infrastructure encompasses tokenization & inference parameters, prompt engineering, and LLM APIs [2] - Tool usage & function calling, agent reasoning (e g, ReAct), task planning & decomposition, and memory management are crucial components [3] - Inter-Agent communication, routing & scheduling, state coordination, and Multi-Agent RAG facilitate collaboration [4] - Agent roles & specialization and orchestration frameworks (e g, CrewAI) enhance workflow construction [4] Trust, Safety, and Scalability - Observability & logging (e g, using DeepEval), error handling & retries, and security & access control are essential for trust and safety [6] - Rate limiting & cost management, workflow automation, and human-in-the-loop controls ensure scalability and governance [6] - Agentic AI features a stacked architecture, with outer layers adding reliability, coordination, and governance [5]