Pattern: Self-Correcting Agent Loops with Guardrail Prompts

Self-correcting agent loops use guardrail prompts to catch errors before they compound across iterations. This pattern prevents hallucinations from multiplying as an agent replans.

Why You Need This Pattern

AI agents that iterate without checks tend to accumulate mistakes. Each turn through the reasoning loop can drift further from your requirements. Guardrail prompts act like circuit breakers, forcing the agent to validate outputs before proceeding.

Implementation Sketch

``json { "agent_loop": [ {"step": "plan", "action": "generate_task_sequence"}, {"step": "validate", "action": "check_against_requirements"}, {"step": "execute", "action": "perform_tool_calls"}, {"step": "guardrail_check", "action": "reject_if_violations_found"} ] } ``

Guardrails should verify: - Outputs match schema constraints - Tool calls respect permission boundaries - Reasoning aligns with safety policies

Pre-LLM vs Post-LLM Guardrails

Pre-LLM guardrails filter inputs before they reach the model. Post-LLM guardrails evaluate outputs and actions. Arthur AI documents this distinction in their Best Practices for Building Agents guide.

When to Skip This Pattern

If your agent only makes one tool call and returns, guardrail loops add unnecessary overhead. Use this pattern when agents iterate multiple times or when correctness is mission-critical.

--- Source: Arthur AI's agent guardrails documentation