How AI Coding Agents Work

The agent loop

An AI coding agent is a large language model connected to tools. The core loop is: receive a task, reason about what to do, call a tool (read file, run terminal command, write to a file), observe the result, and repeat until the task is complete. What makes this powerful is that the agent maintains context across dozens of steps, reading your codebase, understanding your architecture, and making changes consistent with your existing conventions. This is the same loop whether you are using Claude Code in a terminal, Cursor in an IDE, or GitHub Copilot in VS Code.

Context windows

The context window is the most important technical constraint in coding agents. Every file the agent reads, every command it runs, and every result it observes consumes tokens. Claude Code operates with a 200K token context window per conversation turn (with extended thinking for complex tasks), while Cursor and Windsurf use intelligent codebase indexing to work within their context limits. Smaller effective context windows force agents to be more selective about what they read, which can lead to changes that are inconsistent with the rest of the codebase.

Codebase indexing

Codebase indexing is how agents build awareness before starting a task. Tools like Cursor and Sourcegraph Cody index your entire repository and use semantic search to retrieve relevant files when needed. ContextPool takes this further by extracting engineering insights from past sessions and loading them automatically at the start of each new session via MCP, giving agents persistent memory across conversations.

Model Context Protocol (MCP)

Most production-grade agents now support Model Context Protocol (MCP), a standard that lets agents connect to external tools (databases, APIs, project management systems, CI/CD pipelines) in a consistent way. This is what allows an agent to not just write code, but to check Jira for requirements, query your database schema, run tests, and open a pull request, all in a single workflow without switching contexts. Claude Code, Cursor, Cline, and Windsurf all support MCP natively.

Human-in-the-loop controls

Human-in-the-loop controls determine how much you trust the agent to act without approval. Most tools default to showing you proposed changes before applying them. GitHub Copilot suggests inline completions that you accept or reject per-line. Cursor shows multi-file diffs for review before applying. Fully autonomous agents like Devin can operate for hours without check-ins, but the tradeoff is less control over intermediate decisions. Well-scoped backlog items with clear acceptance criteria are safe to delegate fully. Open-ended feature work benefits from more frequent check-ins.

Tool use and real-world actions

The most capable agents in 2026 go beyond reading and writing files. They can browse the web, execute shell commands, manage git branches, interact with APIs, and trigger CI/CD pipelines. According to the Databricks 2026 State of AI Agents report, 97% of database testing and development environments are now built by AI agents. This is a direct result of agents being able to take real actions in connected systems, not just generate text. Claude Code and Aider operate directly in the terminal with full shell access, while Cursor and Windsurf execute within their IDE sandboxes.

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