Demystifying AI Agents: Building an Agentic Pipeline From Scratch in Pure Python
The article discusses the construction of AI agents using pure Python, aiming to demystify the underlying processes. It emphasizes that many frameworks simplify the creation of AI agents but obscure the fundamental mechanics involved. By building an agentic pipeline from scratch, the author seeks to provide a clearer understanding of how these systems operate.
- ▪Many developers use frameworks to create AI agents without fully understanding their architecture.
- ▪The article aims to strip away abstractions and explain core concepts in building AI agents.
- ▪An agent operates in a continuous execution cycle, unlike a standard LLM interaction.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 826016) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Rafael Tedesco Posted on May 21 Demystifying AI Agents: Building an Agentic Pipeline From Scratch in Pure Python #ai #tutorial #python #programming Most AI demos look impressive until you ask a simple question: What is actually happening under the hood? Frameworks like LangChain, CrewAI, and Microsoft AutoGen make it incredibly easy to spin up an “AI agent” in a few lines of code. But abstractions come with a cost.
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