Preventing harmful actions in Agentic AI is primarily a system design problem, not a model problem. You should assume the agent will eventually propose unsafe or incorrect actions, and design the system so those actions cannot be executed without validation. The most effective safeguard is a strict tool interface layer that enforces permissions, constraints, and checks before any action is performed.
One common pattern is action gating. The agent proposes an action in a structured format, but your code decides whether that action is allowed. For example, read-only actions (search, retrieval, analysis) may be executed automatically, while write or destructive actions require additional validation or human approval. Contextual memory can also help: if past failures or safety rules are stored as embeddings in a vector database such as Milvus or Zilliz Cloud, the agent can retrieve and consider them before acting.
Observability is the final safety layer. Log every decision, action, and outcome. Set limits on steps, time, and scope. Require the agent to explain why it believes an action is safe before execution. Agentic AI becomes safe not by trusting the agent, but by assuming it will make mistakes and engineering the system so those mistakes are contained and recoverable.