Claude Code
Claude Code is a terminal-resident AI coding agent developed by Anthropic. It is a CLI tool that enables users to consistently perform codebase comprehension, editing, test execution, and Git operations through natural language instructions.
There are many AI coding assistants that run as IDE plugins, but Claude Code takes a somewhat different approach. It lives in the terminal, reads the entire project's file structure, dependencies, and commit history, then handles everything from file editing to running tests and creating pull requests based on the developer's instructions. It feels almost like having a "pair programmer with full codebase context" right in your terminal. One standout feature is its integration with the Agent SDK. Developers can reuse Claude Code's toolset as a foundation for building custom automation workflows. For example, a sequence like "read an Issue, write a fix, pass the tests, and open a PR" can be automated with human approval checkpoints along the way. It also supports multi-agent functionality, where a lead agent breaks tasks into subtasks and assigns them to multiple Claude Code instances running in parallel, then merges the results. This is particularly powerful for large-scale refactoring and cross-cutting changes. It also supports MCP (Model Context Protocol), so by connecting external services like Supabase or Slack as tools, you can perform database operations and send messages directly from the terminal. My team uses it on a daily basis, and we've genuinely noticed fewer rework cycles — especially for self-review before code reviews and for automating the addition of tests.
Related Terms
Ambient AI
Ambient AI refers to an AI system that is seamlessly embedded in the user's environment, continuously monitoring sensor data and events to proactively take action without requiring explicit instructions.
Context Engineering
Context Engineering is a technical discipline focused on systematically designing and optimizing the context provided to AI models — including codebase structure, commit history, design intent, and domain knowledge.
LoRA
LoRA (Low-Rank Adaptation) is a technique that inserts low-rank delta matrices into the weight matrices of large language models and trains only those deltas, enabling fine-tuning by adding approximately 0.1–1% of the total model parameters.
Let's discuss your needs
Whether it's AI adoption, process improvement, or cost reduction — we're here to help. With 1,850+ client engagements, we'll find the right solution for you.
Free consultation. We'll respond within 24 hours.