The Agentic Feedback Loop

Unlike traditional LLM wrappers, Cursor agents operate within a tight feedback loop inside the IDE. When assigned a task, the agent analyzes the codebase, selects a tool (e.g., readFile, runBash), executes it, and evaluates the output. This cycle repeats for up to 10 turns, allowing the agent to self-correct based on compiler errors or test failures. Because the agent can verify its own work by running code, it is significantly more reliable than text-only generation.

Core Architecture and Constraints

Agents are built using a TypeScript framework that provides direct access to the filesystem and shell. To ensure predictability and debuggability, the SDK enforces two primary constraints:

  • Synchronous Execution: Agents wait for each tool to finish before proceeding, preventing parallel task conflicts.
  • 60-Second Timeout: Any action exceeding 60 seconds will trigger a timeout. Developers must design tasks to be granular (e.g., testing a specific module rather than an entire suite) to avoid hitting this limit.

Building for Production

Effective agents require more than just a prompt; they require structured tool definitions and safety boundaries.

  • Custom Tools: Developers can register domain-specific tools (e.g., calculateProjectMetrics) to extend the agent's capabilities beyond standard file operations.
  • Safety Constraints: Production agents should be restricted using allowedDirectories and forbiddenPatterns to prevent accidental modification of sensitive files like .git/ or node_modules/.
  • State Management: While agent runs are isolated by default, developers can persist conversationHistory to enable multi-stage workflows where the agent builds on previous analysis.

Strategies for Success

  • Prompt Specificity: Vague prompts lead to aimless exploration. High-performing agents require a "roadmap" approach, where the user defines the objective, the specific files to audit, and the criteria for success.
  • Self-Validation: Instruct agents to write and run tests as part of their workflow. This allows the agent to catch its own bugs before the developer reviews the changes.
  • Specialization: Rather than building one "god-agent," use multiple specialized agents (e.g., one for security, one for performance, one for architecture) running in parallel to provide a comprehensive code review in seconds.