All active agents, regardless of the details of where their intelligence comes from, share crucial, never-changing functions like goal-directed loops of action and perception.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• The simple fact that intelligent agents are collectives and do not continue unchanged fulfills a necessary requirement for an open-ended evolution of intelligence.claim0.821
  Connects collective intelligence to evolutionary potential.
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.818
  Abstract and §3, preference learning section.
• In this loop, humans and technology both contribute as agents, and their capacity for care and intelligence can be observed and tracked by focusing on the transfers of their stresses.quote0.804
  Describes the dual SCI loop perspective.
• All intelligent agents—biological, technological, or hybrid—can be assessed via stress-care-intelligence loops regardless of substrate.claim0.798
• Patterns can be seen as active agents in the sense-making process.claim0.796
  Central speculative claim blurring the line between data and algorithms.
• Agents and their interactions are intrinsic to studying information flow in computation, not peripheral to classical information theory.claim0.796
  Central thesis: traditional static information theories fail to capture dynamic interaction necessary for understanding modern computing.
• Open-ended evolution of intelligence is possible because agents are collectives without fixed essencehypothesis0.793
  Follows from observation that intelligent systems lack context-transcendent core; their maxima are not contingent on permanent character.
• Active inference agent with learnable preferences developed a strict preference for goals (score +) when the Frisbee location was encountered first, becoming a goal-seeking agent.finding0.790
  Figure 5.4 and text.