AI Agents vs LLMs vs RAGs vs Agentic AI | Rakesh Gohel

📌 After months of feedback and iteration, we are finally releasing our first technical cohort, "AI Agent Engineering" 🔗 Enrol here: https://lnkd.in/gDEPcXBB Comparing AI Agents with the modern GenAI architectures Let us understand the core difference between them... As GenAI systems become more intelligent, we are looking at a rapid rate of progression. 📌 Starting from level 1, lets see how systems have progressed so far: 1. LLM (Large Language Model) - Context-Free Generation: Produces text purely from prompt input without external retrieval. - Fast & Simple: Easy to deploy with low complexity, but limited in context understanding or integrating new data sources. 2. RAG (Retrieval-Augmented Generation) - Knowledge-Enhanced: Combines LLM output with real-time retrieval from external sources for more accurate, up-to-date responses. - Data-Dependent Precision: Excels at Q&A and knowledge tasks but is sensitive to the quality and structure of underlying data sources. 3. AI Agent - Autonomous Task Execution: Uses planning, reasoning, memory, and tool integrations to complete workflows that need decision-making. - Goal-Oriented Automation: Ideal for well-defined tasks like multi-step data processing or tool-based operations needing structured plans. 4. Agentic AI - Multi-Agent Collaboration: Deploys multiple specialized agents that coordinate, divide labor, and even negotiate to handle complex problems. - Adaptive & Persistent: Supports memory, feedback, and reasoning across agents to tackle large-scale tasks requiring ongoing strategy. 📌 Progression 1. LLM Workflow: - Begins with next-word prediction on static training data—ideal for simple text generation and chatbots with limited context. 2. RAG: - Enhances LLMs by retrieving real-time external knowledge, grounding responses with accurate, up-to-date information. 3. AI Agents: - Introduces planning, memory, and tool use to autonomously execute multi-step workflows with reasoning. 4. Agentic AI: - Evolves into a collaborative multi-agent ecosystem where specialized agents coordinate, share memory, and divide tasks to solve complex problems together. 📌 Use-Cases 1. LLM: For generating text or answering simple, general questions without needing external data. 2. RAG: For retrieving and summarizing up-to-date, domain-specific knowledge during a conversation. 3. AI Agent: For automating single-user tasks that need planning and tool use—like research assistance, report generation, or workflow automation. 4. Agentic AI: For managing complex, multi-step, multi-user processes where multiple specialized agents coordinate as an ecosystem. #fyp #aiagents #llm #genai #aiagents #ai