AI Confidence Score

What Is an AI Confidence Score?

An AI confidence score is a numerical measure (typically 0-100% or 0-1) that represents how certain an AI agent is about the accuracy of its response or the appropriateness of its intended action. High confidence means the agent has strong evidence supporting its answer. Low confidence means the agent is uncertain and the response may be unreliable.

Confidence scores are the primary mechanism for managing AI reliability in production. They determine when the AI answers directly, when it asks clarifying questions, and when it escalates to a human.

How Confidence Scores Work

Confidence scores are derived from multiple signals:

• Retrieval quality: How closely the retrieved knowledge base content matches the customer's question (via semantic search similarity scores)
• Source coverage: Whether multiple sources agree on the answer or only one source was found
• Intent clarity: How clearly the intent recognition system identified what the customer is asking
• Response grounding: How well the generated response is supported by the retrieved evidence (grounding strength)

Why Confidence Scores Matter

Without confidence scoring, an AI agent treats every response with equal certainty — even when it's guessing. This leads to hallucinated responses delivered with false confidence, which is worse than saying "I'm not sure" or escalating to a human.

Well-calibrated confidence scores enable tiered response behavior:

• High confidence (90%+): Respond directly to the customer
• Medium confidence (60-90%): Respond but include caveats, ask clarifying questions, or offer to escalate
• Low confidence (below 60%): Escalate to a human agent rather than risk an inaccurate response

Industry context: In 2024, 47% of enterprise AI users made major business decisions based on hallucinated content — a statistic that underscores the importance of confidence scoring as a safeguard against AI overconfidence.

The Maven Advantage: Calibrated Confidence and Transparency

Maven AGI uses confidence scoring throughout its reasoning pipeline to determine response behavior. When confidence is high, the agent resolves autonomously. When confidence is low, it escalates with full context. Maven's "Thinks Out Loud" feature makes this confidence assessment visible to support teams, providing transparency into why the AI chose to respond, clarify, or escalate.

Maven proof point: Check maintains 85% accuracy across complex financial queries — a domain where confidence calibration is critical because incorrect answers about financial matters carry real consequences.

Frequently Asked Questions

Can confidence scores be wrong?

Yes. Confidence scores are estimates, not guarantees. An AI can be confidently wrong (high score on an incorrect answer) or unnecessarily uncertain (low score on a correct answer). Continuous calibration through QA and feedback loops improves accuracy over time.

Should customers see the AI's confidence score?

Generally no — raw scores aren't meaningful to customers. Instead, the AI should adjust its language based on confidence: "Your refund will be processed within 3-5 business days" (high confidence) vs. "Based on what I can see, your refund should be processed in 3-5 business days, but let me connect you with a specialist to confirm" (medium confidence).

How do you set the right confidence thresholds?

Start conservative (escalate more) and adjust based on data. Monitor the accuracy of responses at different confidence levels and tune thresholds to balance resolution rate with accuracy. Higher-risk domains (healthcare, finance) should use higher thresholds than lower-risk domains.

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