Judement Layer
Governing when the agent acts vs. defers
Governing when the agent acts vs. defers
Determines when agents act independently versus asking permission, balancing autonomy with accountability.
Agentic Use Case Examples
Category
Financial Services & Insurance
Example
Portfolio agent executes trades based on risk models and thresholds for escalation.
Industry
Manufacturing & Energy
Example
Operations agent knows when to alert humans versus act independently.
Industry
Security, Privacy & Risk
Example
Privacy agent chooses when to prompt for consent or silently restrict access.
Industry
Decentralized Systems
Example
Governance agent identifies action thresholds and calls for votes based on logic.
Applying the Judgement Layer in Product Development
1
Solves for: Ambiguity around where the AI can act independently.
Solves for: No shared standard for confidence thresholds or escalation.
Solves for: Teams assume “more autonomy” equals better UX.
Project Kickoffs
1
Solves for: Ambiguity around where the AI can act independently.
Solves for: No shared standard for confidence thresholds or escalation.
Solves for: Teams assume “more autonomy” equals better UX.
Project Kickoffs
1
Solves for: Ambiguity around where the AI can act independently.
Solves for: No shared standard for confidence thresholds or escalation.
Solves for: Teams assume “more autonomy” equals better UX.
Project Kickoffs
2
Solves for: No visibility into when or why the agent makes decisions.
Solves for: Users are blindsided by agent actions, or over-consulted on trivial ones.
Solves for: Lack of escalation patterns leads to inconsistent trust moments.
Audits
2
Solves for: No visibility into when or why the agent makes decisions.
Solves for: Users are blindsided by agent actions, or over-consulted on trivial ones.
Solves for: Lack of escalation patterns leads to inconsistent trust moments.
Audits
2
Solves for: No visibility into when or why the agent makes decisions.
Solves for: Users are blindsided by agent actions, or over-consulted on trivial ones.
Solves for: Lack of escalation patterns leads to inconsistent trust moments.
Audits
3
Solves for: New decision logic bypasses trust boundaries without warning.
Solves for: Agent acts on weak signals without validation.
Solves for: Autonomy scope expands without user consent or clarity.
Feature Reviews
3
Solves for: New decision logic bypasses trust boundaries without warning.
Solves for: Agent acts on weak signals without validation.
Solves for: Autonomy scope expands without user consent or clarity.
Feature Reviews
3
Solves for: New decision logic bypasses trust boundaries without warning.
Solves for: Agent acts on weak signals without validation.
Solves for: Autonomy scope expands without user consent or clarity.
Feature Reviews
4
Solves for: No framework to assess user comfort with AI-driven decisions.
Solves for: Participants can’t explain when they want control vs. delegation.
Solves for: Users feel disempowered or second-guessed by the agent.
User Research
4
Solves for: No framework to assess user comfort with AI-driven decisions.
Solves for: Participants can’t explain when they want control vs. delegation.
Solves for: Users feel disempowered or second-guessed by the agent.
User Research
4
Solves for: No framework to assess user comfort with AI-driven decisions.
Solves for: Participants can’t explain when they want control vs. delegation.
Solves for: Users feel disempowered or second-guessed by the agent.
User Research
Real-World Use of the Judgement Layer
Tesla’s Autopilot makes constant real-time driving decisions, such as when to brake or change lanes. It balances sensor input, road rules, and dynamic conditions to act independently yet safely, demonstrating agentic judgment where the AI must continuously evaluate risk, priority, and context to proceed.
Tesla’s Autopilot makes constant real-time driving decisions, such as when to brake or change lanes. It balances sensor input, road rules, and dynamic conditions to act independently yet safely, demonstrating agentic judgment where the AI must continuously evaluate risk, priority, and context to proceed.
Tesla’s Autopilot makes constant real-time driving decisions, such as when to brake or change lanes. It balances sensor input, road rules, and dynamic conditions to act independently yet safely, demonstrating agentic judgment where the AI must continuously evaluate risk, priority, and context to proceed.
Waymo One autonomous taxis manage complex urban scenarios, handling unprotected turns, navigating obstructions, and predicting pedestrian behavior with real-time AI decision logic.
Waymo One autonomous taxis manage complex urban scenarios, handling unprotected turns, navigating obstructions, and predicting pedestrian behavior with real-time AI decision logic.
Waymo One autonomous taxis manage complex urban scenarios, handling unprotected turns, navigating obstructions, and predicting pedestrian behavior with real-time AI decision logic.
Accenture’s platform allows AI agents to collaborate across enterprise systems and with each other, designing agent-to-agent and agent-to-human interaction norms for enterprise workflows.
Accenture’s platform allows AI agents to collaborate across enterprise systems and with each other, designing agent-to-agent and agent-to-human interaction norms for enterprise workflows.
Accenture’s platform allows AI agents to collaborate across enterprise systems and with each other, designing agent-to-agent and agent-to-human interaction norms for enterprise workflows.
Meta’s content moderation AI makes split-second calls on policy violations, balancing freedom of expression against community standards through learned judgment rules and escalating uncertain cases to human reviewers.
Meta’s content moderation AI makes split-second calls on policy violations, balancing freedom of expression against community standards through learned judgment rules and escalating uncertain cases to human reviewers.
Meta’s content moderation AI makes split-second calls on policy violations, balancing freedom of expression against community standards through learned judgment rules and escalating uncertain cases to human reviewers.
Claude’s Constitutional AI uses built-in ethical guidelines to assess its own outputs, revising responses that violate safety or alignment principles, introducing agentic self-judgment into generative dialogue.
Claude’s Constitutional AI uses built-in ethical guidelines to assess its own outputs, revising responses that violate safety or alignment principles, introducing agentic self-judgment into generative dialogue.
Claude’s Constitutional AI uses built-in ethical guidelines to assess its own outputs, revising responses that violate safety or alignment principles, introducing agentic self-judgment into generative dialogue.