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Agentic Ai

Learn Agentic Ai as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Learn Agentic Ai as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Langchain
Python
Llm
Ai Agents

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Article 1System Design for Agentic AI Systems: From Distributed Systems Principles to ProductionTLDR: Agentic AI systems are distributed systems with non-deterministic workers. If you design them with queue-first execution, explicit state machines, idempotency keys, bounded retries, and strong o18 minArticle 2Low-Level Design for an AI Agent Orchestration Engine: Designing a Stateful Execution FrameworkTLDR: In this guide, we design a stateful, graph-based AI agent execution engine in Java using clean object-oriented principles. By structuring execution as nodes and edges over a shared state, we pre23 minArticle 3Algorithms for AI: Trie, Graph Sorting, and K-Way Merge for LLM SystemsTLDR: Generative AI relies heavily on classic computer science algorithms under the hood. In this guide, we explore how to implement Trie prefix filtering for constrained model decoding, Topological S13 minArticle 4LangChain Tools and Agents: The Classic Agent LoopšŸŽÆ Quick TLDR: The Classic Agent Loop TLDR: LangChain's @tool decorator plus AgentExecutor give you a working tool-calling agent in about 30 lines of Python. The ReAct loop — Thought → Action → Obser21 minArticle 5LangChain RAG: Retrieval-Augmented Generation in Practice⚔ TLDR: RAG in 30 Seconds TLDR: RAG (Retrieval-Augmented Generation) fixes the LLM knowledge-cutoff problem by fetching relevant documents at query time and injecting them as context. With LangChain 19 minArticle 6LangChain Memory: Conversation History and SummarizationTLDR: LLMs are stateless — every API call starts fresh. LangChain memory classes (Buffer, Window, Summary, SummaryBuffer) explicitly inject history into each call, and RunnableWithMessageHistory is th18 min

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