Efficient Agent Scaling through Lightweight Large Language Model Adaptation
Keywords:
agent scaling, lightweight adaptation, large language models, multi-agent systems, AI governance, sustainable infrastructure, fairness, robustnessAbstract
The rapid emergence of language-model-based agents has shifted artificial intelligence from isolated prediction tasks toward persistent, tool-using, and socially embedded computational actors. Scaling such agents efficiently is not simply a matter of increasing parameter counts or deploying more inference replicas; it requires a systems-level rethinking of adaptation, orchestration, memory, governance, and infrastructure. This paper examines lightweight large language model adaptation as a strategic pathway for agent scaling. We argue that low-rank adaptation, prompt and prefix tuning, retrieval augmentation, modular skill libraries, and compact policy heads can reduce the marginal cost of creating specialized agents while preserving enough general competence for open-ended interaction. The analysis connects these adaptation choices to structural trade-offs in latency, energy consumption, robustness, fairness, and institutional accountability. We further consider multi-agent architectures, where lightweight customization enables heterogeneous populations of agents rather than a single monolithic model. Such populations introduce new failure modes, including error propagation, coordination collapse, opaque responsibility chains, and unequal access to adaptation capacity. Through cross-domain examples in software engineering, scientific discovery, customer operations, and public-sector services, the paper shows that efficient agent scaling depends less on one optimization technique than on an integrated socio-technical architecture. We conclude with a research agenda for sustainable, auditable, and equitable agent ecosystems.
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