Intentaligned

Intentaligned is a term used to describe systems, methods, or evaluations that aim to ensure that an agent's actions closely match human intent. In practice, intentaligned designs seek to infer user goals, constrain behavior to safe boundaries, and verify that outcomes reflect the specified objectives.

The word is a portmanteau of "intent" and "aligned." It appears in AI safety and human–AI interaction discussions rather than as a formal technical standard. While not universally standardized, intentaligned concepts are applied across domains such as natural language processing, robotics, and decision-support systems.

Approaches associated with intentaligned design include explicit intent specification (providing goals through user input or formal constraints), robust intent inference (deducing goals from context), value alignment (ensuring preferences match user values), interpretability and verifiability (making decisions and constraints auditable), and human-in-the-loop oversight. Techniques from cooperative inverse reinforcement learning, reward modeling, and constraint programming are often cited as supporting tools.

Evaluation focuses on alignment quality, measured by how often system actions satisfy stated intents, and by safety and user satisfaction metrics. Challenges include ambiguous or evolving user intent, misinterpretation, context leakage, and the risk of exploiting lax constraints. In multi-agent settings, coordinating intent across agents adds complexity.

See also: AI alignment, intent recognition, value alignment, safe AI, human-in-the-loop. References: the term is used in AI safety literature and online discourse; formal standards remain limited.