obtendoos

Obtengoos are a coined term used in discussions of distributed artificial intelligence to describe autonomous software agents that cooperatively obtain, filter, and process data across multiple networked nodes. The term is used primarily in theoretical contexts rather than as a widely adopted standard.

Origin and terminology: The word appears to be a blend of "obtaining" and the plural suffix -os, with some attribution to Portuguese roots (obtenção) meaning "obtaining" or "acquisition." The exact origin remains informal and varies by author.

Characteristics: Obtengoos are described as modular, self-organizing agents with local decision modules and a shared objective. They communicate through lightweight protocols to negotiate task assignments, share results, and maintain consistency. They are designed to operate under heterogeneous resources and to adapt to changing network topologies.

Architecture and methods: Their architecture typically includes sensing (environment and workload awareness), a decision layer (negotiation, task planning, learning), and an action layer (migration, data retrieval, task execution). They may employ reinforcement learning, rule-based policies, and reputation systems to coordinate behavior.

Applications and status: In theoretical models, obtengoos illustrate scalable data gathering, edge computing, and autonomic data management. Experimental work often appears in simulations and academic papers rather than production deployments, and no standard implementation exists.

Advantages and challenges: Potential benefits include improved scalability, fault tolerance, and adaptive resource use. Challenges include security, trust, potential for emergent behavior, coordination overhead, and design complexity.

Related concepts: multi-agent systems, autonomous agents, distributed AI, edge computing, data procurement protocols.