Thread: 7 design patterns to stop AI agents from spiraling into endless tool calls

Ever wonder how to stop AI agents from spiraling into endless tool calls? Today I'm sharing 7 design patterns that keep them in check. Read on for can't-miss insights^[1].

1. Prompting & Context Patterns: Use few-shot, chain-of-thought, and role prompting to guide model behavior and reduce unintended loops. Clear instructions set expectations^[1].

2. Responsible AI Guardrails: Integrate safety checks, fairness rules & output verification to catch harmful or looping content before it derails your system^[1].

3. Loop Agents with Termination: Design workflow agents to run iteratively with defined exit conditions (max iterations or feedback signals) to prevent infinite cycles^[3].

4. AI Ops Monitoring & Versioning: Track performance metrics like latency and token usage, enforce prompt/model versioning, and set rollback strategies to catch looping behavior early^[1].

5. Intelligent Model Routing: Route requests to specialized, cost-effective models based on task complexity. This reduces unnecessary, repeated tool calls^[1].

6. Sequential Orchestration: Use a fixed, linear pipeline where each AI agent processes output from its predecessor. Deterministic steps help avoid uncontrolled loops^[7].

7. Self-Feedback & Planning: Empower agents with self-reflection and planning capabilities to evaluate their outputs and exit repetitive cycles when tasks are complete^[8].