Artificial intelligence deployments at the enterprise edge face a problem that centralized AI architectures are structurally unequipped to solve. Current systems are built on four external dependencies: persistent network connectivity, centralized compute, ephemeral context, and continuous human orchestration through a chat interface. In environments where connectivity is denied, degraded, intermittent, or limited (DDIL), these dependencies do not degrade gracefully. They fail completely. This paper defines a shift in how AI systems are architected for deployment in mission-critical, connectivity-constrained environments. The central concept is the self-sufficient cognitive system: a bounded, deployable cognitive node capable of operating when the link to the enterprise is severed. Self-sufficient is a precise term — it does not mean autonomous; it means independent of persistent internet connectivity. The node loses its tether to the cloud. It does not lose its tether to the human. The architecture introduced here — Alistair Prime in a Box — embeds five capabilities within a single deployable unit: local inference, persistent memory, agentic orchestration, governance and policy enforcement, and execution capability. The core contribution is a structured dependency decomposition model that systematically identifies, categorizes, and eliminates or localizes every external dependency a cognitive system carries, producing a DDIL-tolerant architecture with explicit degradation tiers that preserve function — and governance — as connectivity erodes. Rights envelope: Citation permitted with full attribution. No reproduction, redistribution, or derivative works without written permission. AI/ML training use disallowed. See the citation policy at https://nonsequitur.tech/pubs/citation-policy/ for the full rights envelope. Canonical site URL: https://nonsequitur.tech/white-papers/alistair-prime-in-a-box/ Public archive: yks-pubs/papers/alistair-prime-in-a-box-v1-preprint.pdf
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
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 9, 2026
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.