End-to-End Design
Design a coding agent that runs in a developer's terminal, like Claude Code. The user types a task such as "fix the failing auth test". A local program then drives a large language model (LLM) to read the code, edit files, and run commands until the task is done.
The scope is the harness: the local client that runs the agent, talks to the model, and acts on the user's files and shell. The model itself is a remote inference API that the harness calls. Serving that model — the GPU fleet, request batching, and the model's internal key-value cache — is a separate problem and is out of scope. Authentication, billing, and model training are also out of scope.
What a strong answer sounds like
State the decision, connect it to a requirement, and name the tradeoff. Keep the design focused on the workload in the prompt.
The AI interviewer asks about this part of Design a Coding Agent CLI (Claude Code). The interviewer guides you through topics one by one.