What do you treat as the first real safety gate before letting an agent take actions on its own?
Summary
Discusses a three-stage safety rollout for AI agents (observe, propose, execute within bounds) to separate reasoning from execution trust, and asks the community about their first hard gate for reducing failures.
Similar Articles
What's your biggest fear about letting an agent take real actions in production?
A developer shares concerns about deploying AI agents that perform real actions in production, such as API calls and data manipulation, and asks the community about their fears and mitigation strategies like guardrails and human approval.
What guardrails do you add before launching an AI Agent app publicly?
A developer reflects on critical safety measures—such as spending caps, rate limits, and fallback models—that should be in place before launching an AI Agent app publicly to avoid hidden costs and unexpected behaviors.
Who gave your AI agent authority?
Discusses the security gap in AI agent workflows where agents assume human oversight at critical steps, and proposes a runtime control plane that enforces permissions and requires human approval for destructive actions, demonstrated with a Tandem demo.
@dabit3: Now that agents can act, we ask: when should they run, what can they touch, how is their work checked, and what context…
The author proposes Automation Engineering as a discipline for designing triggers, guardrails, and success checks to make AI agents safe and reliable without constant human oversight.
agent safety probably starts with boring permission design
Discusses the importance of boring permission design as a foundational element for ensuring safety in AI agents.