Aug 2026· Conference on Computer Science and Information Systems· pp. 25-32· 0 citations· 61 references
Abstract
Agentic AI is shifting AI applications from passive model inference to goal-driven, tool-using, and collaborative autonomous systems. Yet, current deployments remain concentrated in data centers or powerful personal devices. This paper provides a research roadmap for enabling large-scale, enterprise-oriented agentic AI across the computing continuum, from cloud to edge, IoT, and emerging hardware platforms, where heterogeneity, mobility, energy constraints, and governance requirements fundamentally reshape agent design and operation. We argue for a continuum-native agent paradigm that decomposes agents into intelligence, persona, and memory, allowing their independent placement, migration, and replication. Building on this abstraction, we structure the open research space into three pillars (agent operation, agent connectivity, and agent trustworthiness) and elaborate on potential research directions and challenges. Finally, we outline evaluation directions grounded in representative industrial use cases and benchmarks. The roadmap aims to guide research and development toward enabling the deployment of agentic AI as first-class workloads on the cloud-edge continuum.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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