Tech • IA • Crypto
How Claude Code Works
5/10TL;DR
AI coding agents operate through iterative “agentic loops” that gather context, execute tools, and verify results, enabling them to act autonomously rather than just generate text.
KEY POINTS
Agentic Loop Execution
Cloud-based coding agents function through a continuous loop: interpreting a prompt, gathering relevant context, taking action, and verifying results. This cycle repeats until the task is successfully completed. The system does not stop at generating an answer but actively works toward a verifiable outcome.
Context Gathering and Memory Limits
These systems rely on a defined context window that stores conversation history, file contents, and command outputs. When the limit is reached, the system compresses or summarizes information to retain essential details while freeing space. This allows extended workflows without exceeding memory constraints.
Tool-Driven Capabilities
Tools are central to agent functionality, enabling actions such as reading files, searching the web, or executing commands. Unlike traditional chat-based AI that only produces text, agents decide when to invoke tools to progress toward a solution. Semantic reasoning determines which tools to use and when.
Autonomous Action and Verification
After executing actions like editing code or running commands, the system evaluates whether the results meet the original objective. If the outcome falls short, it re-enters the loop and tries alternative approaches, improving reliability through iterative verification.
User Interaction and Control
Users can intervene during execution by adding context, redirecting the process, or interrupting tasks. This interactive layer ensures that the system remains aligned with user intent while maintaining a degree of autonomy.
Permission Modes and Safety
Permission settings regulate how much control the agent has. Default modes require approval before modifying files or executing commands, while more permissive modes allow automatic actions. Increased autonomy introduces higher risk, as errors may occur before user intervention.
Planning and Read-Only Modes
Specialized modes enable agents to analyze a codebase and formulate a plan before taking action. These modes rely on read-only tools, reducing risk while improving task clarity and execution strategy.
CONCLUSION
Agentic coding systems represent a shift from passive AI responses to active problem-solving tools, combining context management, tool use, and iterative verification to perform complex tasks with increasing autonomy.