Agents in Claude Code: How They Work and How to Set Up Your Own

⚡ The Gist in 5 Seconds - Agents in Claude Code operate in isolated parallel contexts: each one sees only its own task and returns a finished result to the main chat. - Available right after installing Claude Code as an extension in the free VS Code — no programming required. - For routine scenarios, you can create persistent agents via files in ~/.claude/agents/ or have Claude Code generate them itself, though the instructions are worth reviewing. ### 🔍 What Was Found Claude Code is an app from Anthropic that runs the neural network not in a browser but on your computer: it can see files, search the internet, and save documents. Any neural network has a ceiling on working memory — the context. When reading 13 long articles in a single chat, by the fifth one the context overflows and the model starts confusing the beginning with the end. Agents solve this through isolation: each one receives a task, works in its own context, and only the final result arrives in the shared chat. This makes it possible to run agents in parallel — total time equals the longest task, not the sum of all of them. Built-in agents work without any setup: just ask to parallelize a task, and Claude Code will split the work itself. For recurring scenarios, text files are created in the ~/.claude/agents/ folder with a name, description, and instructions. You can write the file manually, ask Claude Code to generate it, or download ready-made libraries from GitHub — for example, wshobson/agents (over 200 agents) and contains-studio/agents (a department of marketers, analysts, and designers). In a real-world example, four agents processed 13 articles in parallel: three read the materials for lists, while the fourth searched vc.ru for similar articles with 50,000+ views. The work took 7 to 30 minutes — instead of a day and a half by hand. ### 💡 Why It Matters The main practical benefit is saving time and eliminating manual switching between tasks. While the agents work, you can discuss other topics in the same chat. Analyzing the style of 13 articles with verbatim quotes and structure fit into 30 minutes, five of which were task setup. Similar tasks include collecting target-audience feedback (a table with hundreds of verbatim quotes and sources) and preparing branded presentations (a draft in under an hour instead of one or two days). This moves routine operations into the category of automatable ones. ### 🧩 Context Claude Code emerged as an alternative to the browser chat: the neural network runs locally, with access to the file system and the internet. Built-in agents are part of this paradigm: they address the fundamental context limitation, allowing large volumes of information to be processed without loss of quality. Setting up persistent agents via simple files makes the tool flexible for specific tasks — from style analysis to branded layouts. Practice shows the barrier to entry is minimal: no coding needed, just text instructions.

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