Converging Agentic AI, Data Mesh, and Lightweight Language Models for Resilient System-of-Systems Automation
DOI:
https://doi.org/10.63646/ATTP4263Keywords:
Agentic AI; Data mesh; Lightweight language models; System-of-systems automation; Enterprise architecture; Federated governance; AI safety; Multi-agent systemsAbstract
This article develops a system-of-systems framework for resilient enterprise automation by integrating three fast-developing technological streams: agentic artificial intelligence, domain-oriented data mesh governance, and lightweight language models. Contemporary language-model agents are increasingly able to plan tasks, select tools, call functions, and synthesize multi-source information, but their deployment in complex enterprises remains constrained by data silos, legacy systems, cross-domain compliance requirements, ambiguous instructions, privacy risks, and cascading failure modes. Drawing on the architectural ideas of multi-context agent coordination and mesh-oriented governance, the article proposes a lifecycle framework in which each enterprise domain exposes approved capabilities as governed functions while retaining authority over local data, policies, and risk controls. Lightweight domain-specific models are positioned as the operational substrate for low-latency, privacy-preserving, and cost-sensitive automation, while larger models remain useful for controlled synthesis and exception handling. The paper contributes a six-stage lifecycle model, a domain-capability governance map, a comparative scenario analysis of four automation architectures, and a risk matrix for secure multi-domain agent deployment. The analysis suggests that the strongest architecture is not a single general-purpose agent connected to all enterprise systems, but a federated mesh of domain-specialized agents supported by local data ownership, policy-aware routing, audit trails, fallback controls, and model portfolios. The article concludes with an implementation roadmap and research agenda for benchmarking, security evaluation, human oversight, and responsible governance in future system-of-systems automation.
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