How can we ensure safety and prevent hallucinations in autonomous Agentic AI systems?

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Safety in agentic systems is all about "constrained autonomy." We use a library called Guardrails AI or NeMo Guardrails to validate the output before it hits the API. A common pattern is the "Double-Check" agent: one agent proposes an action, and a second, more restricted agent reviews it against a set of business rules. For HITL, you should set "confidence thresholds." If the agent is less than 90% sure about a decision, it flags it for a human. This keeps the boring stuff automated while ensuring high-risk tasks are supervised by a real person.

   Answered 2024-11-07 by Michelle Wright

What kind of "irreversible actions" are you most worried about? Is it mostly about deleting data, or are you concerned about agents communicating incorrectly with clients?

   Answered 2024-11-09 by Thomas Baker

• Thomas, it’s actually both. I’m terrified of an agent sending a legally binding contract to a client with the wrong terms because it misinterpreted a previous email. We need a way to ensure that any external-facing communication or database "write" operation goes through a verification layer that is deterministic and doesn't rely solely on the LLM's current "mood."

     Commented 2024-11-10 by James Peterson

Just use a "Dry Run" mode. Let the agent generate the plan and the code, but require a manual button click to actually execute the final script in production.

   Answered 2024-11-12 by Barbara Young

• Barbara's suggestion of a "Dry Run" is a solid first step. It’s a great way to build trust with the stakeholders before you flip the switch to full autonomy.

     Commented 2024-11-13 by Michelle Wright