IANAs

IANAs, short for Intelligent Autonomous Networked Agents, is a term used in computer science and AI to describe software agents designed to operate with a high degree of autonomy within connected environments. In practice, IANAs are intended to act in dynamic settings, coordinate with other agents and humans, and adapt their behavior based on experience and feedback.

Architecture and capabilities: An IANA system typically includes perception or sensing, a decision-making module, action or effectors, and a communication layer for interoperability. They rely on a mix of machine learning, planning, rule-based reasoning, and sometimes symbolic AI to balance flexibility with interpretability. Autonomy ranges from task-level to goal-level control, with governance mechanisms to avoid unsafe actions.

Applications: IANAs are proposed for complex domains such as supply-chain optimization, energy systems management, autonomous robotics, traffic and logistics, healthcare assistance, cybersecurity, and customer-service automation.

Taxonomy and governance: Researchers distinguish collaborative IANAs that coordinate to achieve common objectives, competitive IANAs that act in markets or adversarial settings, and hybrid configurations. Governance and safety are active research areas, including alignment with human values, explainability, auditability, and resilience to manipulation.

Relationship to other concepts: The term is sometimes used interchangeably with multi-agent systems or autonomous agents, though some scholars reserve IANA for networked and interoperable agents that emphasize collaboration across platforms. History and usage: The phrase is not standardized; it has appeared in AI literature since the 2010s, with definitions varying across subfields.