Beyond Agents: A Task-Centric Framework for Intelligent and Autonomous Task Fulfillment
Abstract
Artificial intelligence systems are increasingly organized around agents, tools, workflows, and specialized services. Although these approaches enable increasingly sophisticated forms of automation, they may also impose an unnecessary conceptual burden on users, who must often identify appropriate systems, formulate instructions, select tools, or coordinate multiple components. This paper proposes a task-centric perspective in which the primary object of interaction is neither a specific artificial intelligence agent nor a predefined workflow, but the successful fulfillment of a user's underlying need. Under this perspective, users express desired outcomes, while an intelligent system determines how those outcomes should be achieved by autonomously selecting, creating, coordinating, or replacing agents, tools, human participants, and external services as required. The paper introduces the concept of Task-Centric Autonomous Fulfillment (TCAF) and develops a conceptual framework for outcome interpretation, task decomposition, resource selection, execution, monitoring, and completion verification. A formal optimization model is presented that incorporates task quality, completion probability, time, cost, uncertainty, risk, coordination overhead, and human intervention. The framework distinguishes task fulfillment from conventional agent-centric interaction, fixed workflow automation, and isolated tool invocation without assuming that these existing approaches are incompatible with the proposed perspective. Research propositions, evaluation metrics, experimental designs, and implementation challenges are outlined. The paper argues that a task-centric architecture provides a useful foundation for investigating AI systems that organize intelligence around what people need accomplished rather than which artificial intelligence components they must operate.
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BibTeX
@misc{zhang2026beyond,
title = {{Beyond Agents: A Task-Centric Framework for Intelligent and Autonomous Task Fulfillment}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23591684},
url = {https://zenodo.org/records/23591684},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond Agents: A Task-Centric Framework for Intelligent and Autonomous Task Fulfillment
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23591684
UR - https://zenodo.org/records/23591684
AB - Artificial intelligence systems are increasingly organized around agents, tools, workflows, and specialized services. Although these approaches enable increasingly sophisticated forms of automation, they may also impose an unnecessary conceptual burden on users, who must often identify appropriate systems, formulate instructions, select tools, or coordinate multiple components. This paper proposes a task-centric perspective in which the primary object of interaction is neither a specific artificial intelligence agent nor a predefined workflow, but the successful fulfillment of a user's underlying need. Under this perspective, users express desired outcomes, while an intelligent system determines how those outcomes should be achieved by autonomously selecting, creating, coordinating, or replacing agents, tools, human participants, and external services as required. The paper introduces the concept of Task-Centric Autonomous Fulfillment (TCAF) and develops a conceptual framework for outcome interpretation, task decomposition, resource selection, execution, monitoring, and completion verification. A formal optimization model is presented that incorporates task quality, completion probability, time, cost, uncertainty, risk, coordination overhead, and human intervention. The framework distinguishes task fulfillment from conventional agent-centric interaction, fixed workflow automation, and isolated tool invocation without assuming that these existing approaches are incompatible with the proposed perspective. Research propositions, evaluation metrics, experimental designs, and implementation challenges are outlined. The paper argues that a task-centric architecture provides a useful foundation for investigating AI systems that organize intelligence around what people need accomplished rather than which artificial intelligence components they must operate.
ER -