Agent is aI system that takes actions to achieve goals, often across tools. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0011, within the Workflow family. The core principle: aI system that takes actions to achieve goals, often across tools. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
Scientific Definition
AI system that takes actions to achieve goals, often across tools.
Plain-English Definition
AI system that takes actions to achieve goals, often across tools.
Feynman Explanation
Chat is reactive. Agents are autonomous. The risk profile is different.
Core Principle
AI system that takes actions to achieve goals, often across tools.
Mechanisms
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Inputs (Triggers)
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Outputs (Behaviors)
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Behavioral Signature
Chat is reactive. Agents are autonomous. The risk profile is different.
Examples
- Customer support agents, coding agents, research agents.
- Major emerging category.
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Original analysis from The Incentives Lab — how this element behaves inside real payoff structures.
Why this element matters to incentive design
This is one of the elements leaders describe as a values gap. It is a payoff gap. The mechanism underneath it operates in the Incentives dimension — what makes behavior more or less likely?. You can recognize it in the field by its signature: chat is reactive. Agents are autonomous. The risk profile is different. Every element in the Incentives dimension changes the perceived payoff of an action before the action happens, which is exactly where incentive design has leverage.
How it gets exploited
Left undesigned, major emerging category. It is amplified whenever major emerging category. Inside organizations that shows up as major emerging category. The pattern is the same one Goodhart's Law describes: the measurable proxy attracts the effort, and the purpose behind it quietly loses funding.
How the Lab designs around it
The redesign move is to sandbox first. Define guardrails. Tight observability. The test of any redesign here is simple: after the change, can you name what the organization is now doing less of? If not, the payoff structure did not actually move.
Famous Experiments
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Design Principles
- Sandbox first. Define guardrails. Tight observability.
Measurement Approaches
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Evidence
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The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Sandbox first. Define guardrails. Tight observability.
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Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
Designing processes from scratch around AI capability.
The relationship between inputs and outputs changes.
How much input the model can process at once.
Underlying data distribution changes over time.
Vector representation of content for similarity and search.
Evaluation suites that no longer reflect real-world conditions.
Systematic testing of model quality, safety, and capability.
Models learning from examples in the prompt.
How much delay the user experience tolerates.
Train a smaller model to imitate a larger one.
Performance degradation as real-world data shifts.
Multiple specialized agents coordinating on tasks.
Where Agent is cited in the corpus
Essays, field guides, and diagnostics from The Incentives Lab that apply this element.
- Field guideIncentives: definition, types, examples
The parent field guide for this element.
- ReferenceThe laws of incentives
Goodhart, Campbell, and the Cobra Effect.
- EssayAI Agents Inherit Your Incentives
How this element propagates into automated systems.
- EssayThe Perverse Incentives Hiding in Your KPIs
The measurement failure mode for this element.
- CourseIncentives 101
The free ten-part primer on reading a payoff structure.
- ReferenceThe incentive glossary
Definitions for every mental model, bias, and fallacy in the corpus.
- ReferenceThe Periodic Table of Human Behavior
The full 1,267-element map this page belongs to.
Questions about Agent
- What is Agent?
- Agent is aI system that takes actions to achieve goals, often across tools. It sits in the Incentives dimension (INC) of the Human Behavior Taxonomy™ as element HBT-INC-0011, within the Workflow family. The core principle: aI system that takes actions to achieve goals, often across tools. In incentive terms, it matters because it changes the payoff people perceive before they choose — which means it can be designed for, or exploited.
- What is an example of Agent?
- Major emerging category. The Incentives Lab catalogs everyday, organizational, and historical instances of this element on its Human Behavior Taxonomy™ page (HBT-INC-0011).
- How is Agent exploited?
- Major emerging category.
- How do you design around Agent?
- Sandbox first. Define guardrails. Tight observability.
- Which behavioral dimension does Agent belong to?
- Agent is classified in the Incentives dimension (INC) of the Human Behavior Taxonomy™, family "Workflow", class "AI-Behavioral Coupling". Its permanent identifier is HBT-INC-0011 and its evidence grade is C.