Spaces:
Sleeping
Sleeping
Prabhas Jupalli
UI branding overhaul, UI background updates, backend summary crash fix, and GridMatrix visualization refinements
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| "summary": "Agentic AI refers to AI systems that act as autonomous agents\u2014they don\u2019t just respond to prompts, but plan, decide, and execute tasks independently to achieve goals.\n\n\ud83e\udde0 Core Idea\n\nTraditional AI = reactive (you ask \u2192 it answers)\nAgentic AI = proactive (it decides \u2192 acts \u2192 adapts)\n\n\u2699\ufe0f Key Components\nGoal Understanding\nInterprets high-level objectives (e.g., \u201cplan a trip\u201d)\nPlanning\nBreaks goals into smaller steps (task decomposition)\nReasoning\nChooses best actions using logic or learned patterns\nMemory\nStores past interactions (short-term + long-term)\nTool Use\nCalls APIs, runs code, browses, etc.\nAutonomy Loop\nObserve \u2192 Think \u2192 Act \u2192 Repeat\n\ud83d\udd01 How It Works (Agent Loop)\nReceive goal\nAnalyze & plan steps\nExecute actions (e.g., search, compute)\nEvaluate results\nAdjust strategy until goal is achieved\n\ud83d\ude80 Examples\nAI coding assistants that debug and run code end-to-end\nAutonomous research agents gathering and summarizing info\nPersonal assistants that schedule, email, and book services\nMulti-agent systems collaborating on tasks\n\ud83e\udde9 Types of Agentic AI\nSingle-Agent Systems \u2013 one AI handles everything\nMulti-Agent Systems \u2013 multiple AIs collaborate or compete\nHuman-in-the-Loop Agents \u2013 AI acts but humans approve key steps", | |
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| "Incontext Learning", | |
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| "ai_analysis": "Learning profile enriched by modules like Basics. Stage 20 achieved with 1939 points.", | |
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