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Fundamentals of all AI agents, must know. I’ve explained the key components below. 1. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 These are the 𝗰𝗼𝗿𝗲 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 behind the system. Receive input → Process language → Generate response → Perform reasoning → Return output Eg: GPT (OpenAI), Claude, Gemini, Mistral 2. 𝗔𝗴𝗲𝗻𝘁 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 This layer helps 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝗮𝗴𝗲𝗻𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿. Receive task → Break into steps → Manage workflow → Coordinate tools → Execute actions Eg: LangGraph, CrewAI, AutoGen, Haystack, LlamaIndex 3. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 Agents use memory to 𝗿𝗲𝘁𝗮𝗶𝗻 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. Store interaction → Maintain state → Retrieve past data → Update memory → Improve responses Eg: Conversation State, Key-Value, Episodic, Knowledge Graph Memory 4. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Used to store and search 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀. Convert data → Store vectors → Perform similarity search → Retrieve relevant info → Return context Eg: Pinecone, Weaviate, Qdrant, Milvus, Chroma 5. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 Multiple agents can 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲. Assign roles → Distribute tasks → Communicate between agents → Aggregate outputs → Final result Eg: Role-based, Supervisor–Worker, Swarm, Hierarchical agents, Peer-to-Peer (A2A) 6. 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 Systems require 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗮𝘁𝗮. Collect data → Parse inputs → Clean structure → Transform format → Store for usage Eg: Browser Agents, Firecrawl, Docling, Kafka, Webhooks 7. 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 Defines how agents 𝘁𝗵𝗶𝗻𝗸 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝗱𝗲. Analyze problem → Generate plan → Evaluate options → Select best path → Execute step-by-step Eg: ReAct, Plan-Execute, Tree of Thoughts, Graph of Thoughts, Reflexion. 8. 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 Transforms text into 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝘃𝗲𝗰𝘁𝗼𝗿𝘀. Input text → Encode into vectors → Capture meaning → Store representation → Enable retrieval Eg: BGE, SBERT, OpenAI Embeddings 9. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 & 𝗥𝘂𝗻𝘁𝗶𝗺𝗲 Where the system actually 𝗿𝘂𝗻𝘀. Deploy services → Manage containers → Scale workloads → Handle requests → Ensure reliability Eg: Docker, Kubernetes, Temporal, Airflow, Serverless 10. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Tracks and improves 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. Monitor outputs → Measure accuracy → Detect errors → Analyze logs → Improve system Eg: RAGAS, TruLens, LangSmith, Promptfoo, Human-in-the-loop 11. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 Ensures the system stays 𝘀𝗮𝗳𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗱. Apply policies → Validate outputs → Enforce limits → Prevent unsafe actions → Maintain compliance Eg: Policy Enforcement, Tool Permissioning, Output Validation, Cost & Latency Limits, Compliance Controls You can use this to start building AI agent systems for real-world applications, pretty basic but must know for everyone. ✅ Repost for others because everyone should know this today.