ai-cost-guardrail-designer

What problem does it solve?

This Skill designs the controls that prevent an LLM feature from becoming a denial-of-wallet or denial-of-service risk. It helps teams bound cost, usage, and agentic loops so AI behavior stays predictable, attributable, and safe under load.

Core Features & Use Cases

• Per-request limits: Set input and output token caps, context bounds, and timeouts so no single request can run unbounded.
• Tenant-scoped budgets and rate limits: Add per-user, per-tenant, and per-plan consumption controls that protect shared capacity from noisy neighbors.
• Agent safety controls: Cap iterations, tool calls, recursion depth, and retries to stop runaway loops and retry storms.
• Fail-safe operating modes: Define kill switches, degraded fallbacks, and burn-rate alerts so the feature degrades safely instead of failing open.
• Use case: A product team adds an AI summarization endpoint and needs guardrails for token-drain abuse, budget exhaustion, and alerting before monthly spend is gone.

Quick Start

Ask for a guardrail design for your AI feature that includes token caps, tenant budgets, loop bounds, a kill switch, degraded fallback behavior, and attributable cost telemetry.