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Risk-based approach

As explained in The Art of AI Product Development

Risk-aware decision making is the practice of evaluating potential consequences and applying stricter controls to high-stakes actions.

Risk-based decision making makes an AI agent treat actions differently according to their potential consequences. Minor errors may proceed, while high-stakes or irreversible actions require stronger checks and human confirmation. section 9.2.2

Apply stricter controls when an action could cause substantial or irreversible harm:

  • Flag transactions above a defined threshold for human approval. section 9.2.2
  • Cross-check proposed actions against previous transactions to detect anomalies. section 9.2.2
  • Require multistep verification before executing irreversible actions, supported by rollback mechanisms and audit logs. section 9.2.2

Finance agent

A finance-focused agent should not initiate a large fund transfer from one ambiguous instruction. It should flag the transfer, compare it with prior activity, obtain approval, and verify the action before execution. section 9.2.2

How The Art of AI Product Development covers it

How deep it goes
Defines consequence-sensitive controls and gives a three-step finance example, without specifying implementation details.
Examples
Uses ambiguous fund transfers, transaction thresholds, previous-transaction checks, and multistep verification.
What it assumes
Assumes familiarity with AI agents and task execution while establishing why actions need different levels of control.
What you can do after
Operate agents with approval gates, anomaly checks, verification steps, rollback mechanisms, and audit logs.
How it connects
Connects agent planning with human approval, anomaly detection, rollback mechanisms, and audit logs.

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This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.