A practical guide to building production-ready Anthropic agent loops with Claude. Covers the core loop pattern (send task, get tool call or answer, execute tool, repeat), plus key engineering concerns: writing testable agent goals, keeping system prompts lean, designing strict tool schemas, implementing hard stop conditions (max turns, timeouts, cost budgets, repeated-call detection), scoping tool permissions by risk level, structured error handling, tracing for debuggability, and evaluation beyond happy paths. Includes a Python skeleton, before/after prompt refinement examples, and a production readiness checklist.
Table of contents
The minimum Anthropic agent loopDefine the agent goal in plain, testable termsKeep the system prompt smallDesign tool schemas Claude can use correctlyBuild the loop in application codeAdd hard stop conditionsLock down tool permissionsMake state visible and debuggableHandle failed tool calls as first-class eventsDo not evaluate only happy pathsRefine prompts with before and after examplesWhen to use multiple agentsProduction checklist1 Impression