ai-agent-guardrailslisted

# AI Agent Guardrails LLMs make confident wrong decisions at scale. The cost of one wrong decision used to be one wrong commit; the cost of one wrong decision by an agent loop can be 30 wrong commits, 100 deleted DB rows, or a whole production site refactored into nonsense in 90 seconds. This skill is a design checklist — not a runtime tool. It tells you **what to put in place before** giving an agent write access to anything that matters. ## When to invoke - Designing a new agent, scheduled job, or autonomous workflow - Granting an existing LLM access to a higher-tier credential or new tool - After an agent did something unintended (start here; do not just tighten one prompt) - Reviewing a third-party agent (e.g. an MCP that takes broad actions) before connecting it ## The core idea — blast radius Classify every action an agent can take by what happens if it fires when it should not have. | Tier | Example | Reversible? | Required guard | |---|---|---|---| | 1 | Read a local file, run a query | Trivially | None | | 2 | Modify a single local file, write to a sandbox | Yes, with backup | Backup before action | | 3 | Mutate a staging service or shared dev resource | Recoverable in minutes | Dry-run mode + explicit confirm | | 4 | Production-data write, customer-visible change | Recoverable in hours, with effort | Approval gate + audit log + rollback plan | | 5 | Send mail, spend money, modify DNS, deploy, push to main | Sometimes irreversible, externally visible | Out-of-