agents-agent-loop-guardrailslisted

# Add guardrails to autonomous agent loops > Bound agent autonomy with step budgets, spend caps, human gates, and checkpointing so failures stay cheap and debuggable. **Track:** 🤖 AI Engineering · **Domain:** AI Agents · **Level:** advanced · **~45 min** **Who this is for:** AI Engineers, ML Engineers, LLM App Developers, Agent Builders ## When to Use This Skill Bound agent autonomy with step budgets, spend caps, human gates, and checkpointing so failures stay cheap and debuggable. Use it whenever a matching task appears in conversation — the agent loads these instructions on demand. ## Steps 1. Set hard max-step and max-token budgets; treat exhaustion as a normal outcome 2. Checkpoint full state after every step so runs resume or replay cleanly 3. Require explicit human approval before irreversible external actions 4. Detect loops: identical tool+args twice means intervene or abort 5. Emit structured events per step for tracing and postmortems 6. Run a canary suite of risky tasks nightly and alert on behavior drift ## Common Pitfalls - Unbounded retry loops burning budget on a failing subgoal - Approving via broad allowlists instead of per-action gates ## Success Signals - Mean steps-to-completion trending down - 100% of destructive actions behind approval ## Commands **Install with skills CLI** ```bash npx skills add aniruddhaadak80/skills --skill agents-agent-loop-guardrails ``` **Install globally** ```bash npx skills add aniruddhaadak80/skills --skill agent