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1.6 Foundations

Definition

flowchart TD %% --- Styles (safe) --- classDef env fill:#f7e8f0,stroke:#c2185b,stroke-width:2px,rx:12,ry:12,color:#111; classDef agent fill:#e8f7f0,stroke:#0f9d58,stroke-width:2px,rx:12,ry:12,color:#111; classDef comp fill:#e6f0fb,stroke:#1a73e8,stroke-width:1.5px,rx:8,ry:8,color:#111; classDef action fill:#fff5cc,stroke:#eab308,stroke-width:1.5px,rx:8,ry:8,color:#111; %% --- Environment --- subgraph ENV["Environment"] PERCEPTS["Percepts"] ACTIONS["Actions"] end class ENV env %% --- Agent --- subgraph AG["AI Agent"] S["Sensors<br/>(inputs, APIs, webhooks)"] PERCEPTION["Perception Layer<br/>(preprocess, embed)"] KB["Knowledge Base / Memory<br/>(facts, vectors, state)"] REASON["Reasoning Engine<br/>(LLM+prompts, rules, RL)"] PLAN["Planning & Goals<br/>(decompose, schedule)"] LEARN["Learning Module<br/>(feedback, fine-tune, RL)"] ACT["Actuators<br/>(APIs, commands, outputs)"] end class AG agent class S,PERCEPTION,KB,REASON,PLAN,LEARN,ACT comp %% --- Flow --- PERCEPTS --> S --> PERCEPTION --> KB PERCEPTION --> REASON KB --> REASON REASON --> PLAN --> ACT LEARN --> KB LEARN --> REASON ACT --> ACTIONS

Agentic Architecture

AI Agents vs. Agentic AI

Agentic Workflows

Retrieval Strategies for Agents

LLM Foundation

Architecture Layers

Arch

Learning Resources

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