The article proposes a four‑layered defense strategy for mitigating risks posed by autonomous AI agents. Layer 1 focuses on input guardrails: every piece of data entering the model is validated against schemas, length limits, and allowlists, screened for jailbreak or indirect injection attempts hidden in ingested documents, and stripped of personally identifiable information before inference; failures trigger a fail‑closed response that aborts the protected action. Layer 2 constrains the agent’s toolset through least‑privilege principles, maintaining a minimal list of actions and splitting the agent if the list expands; each tool is assigned a risk rating (low, medium, high) and high‑risk operations such as refunds, deletions, payments, or external messages require an approval gate tied to the exact action’s parameters (order ID, amount, idempotency key) and re‑validated if state changes, while hard caps limit steps, token usage, cost, and execution time per task. Layer 3 validates outputs by enforcing schema conformity, performing grounding checks against retrieved sources, and scoping responses to the agent’s intended domain to detect overreach. Layer 4 adds runtime observability: logs are redacted to avoid leaking raw data, latency, error rates, tool‑call patterns, and per‑task cost are monitored, and a human‑operable kill switch provides an immediate stop mechanism. The approach delegates commodity risks like toxicity and prompt injection to managed services (e.g., Bedrock Guardrails, Azure AI Content Safety) while custom code enforces business‑logic safeguards such as approval workflows and refund limits.