What If an AI Agent Could Say “I Don't Know”?
ENGINEER: My AI agent sometimes gives confident but incorrect answers. I need to eliminate hallucinations.
PO: What if your AI didn't have to answer every question?
ENGINEER: But answering questions is its job.
PO: Is its job to generate answers or deliver trustworthy outcomes?
ENGINEER: Trustworthy outcomes.
PO: Then could refusing to answer sometimes be the correct decision?
ENGINEER: Interesting. I could introduce evidence checks, abstention thresholds, and human escalation.
PO: But how will you know when the AI should remain silent?
ENGINEER: By testing when it answers correctly, when it guesses, and when it refuses unnecessarily.
PO: Now you're engineering trust, not just generating responses.
🔬 ENGINEERING REALITY CHECK
Selective prediction and abstention are established machine learning concepts. They offer promising approaches to reducing unsupported AI responses.
But abstention alone does not eliminate hallucinations. Its effectiveness must be evaluated.
The engineering experiment:
Compare two AI agent configurations:
A. Always Answer — Respond to every question.
B. Answer or Abstain — Respond only when predefined evidence and decision criteria are satisfied.
Measure:
• Incorrect-answer rate
• Correct-answer coverage
• Unnecessary refusals
• Latency and cost
THE NEW DIRECTION
What if we evaluated AI agents not only by the answers they produce, but also by the answers they wisely choose not to produce?
POwer Conversations explores how provocative questions can open new engineering possibilities — followed by evidence, experimentation, and verification.
💬 AI Engineers: Should knowing when NOT to answer become a core capability of every AI agent?
POwer Conversations | AI Engineer
Provocation → Exploration → Verification → Innovation
AIYING — Accomplishing with AI.
#POwerConversations #AIEngineering #AIAgents #LLM #AIHallucinations #ResponsibleAI #LateralThinking #AIInnovation #AIYING
