## 2 Related Work
### Limitations of the Existing Surveys  
AI agents backed by large language models now draft, verify, and correct labels with minimal human effort. When linked in a guided workflow, these agents accelerate annotation, boost consistency, and let human experts focus on hard edge cases. This study is unique because it frames labeling as a closed-loop multi-agent engineering problem. It describes single, dual, and multi-agent workflows that plan, label, and self-audit in one continuous cycle. One study focuses on enlarging corpora through image, text, and paired-data transformations, yet it never addresses who or what produces the labels [22]. [PERSON] et al. [23] cover the full model lifecycle from pre-training through alignment, but annotation quality is listed only as an open challenge. Another study automates labels with a single LLM \"oracle\", omitting the negotiation and reviewer roles that an agent-centric pipeline would require [24]. Some studies showed that classical machine learning pipelines face similar cost versus quality tensions, yet their solutions remain limited to wearable-sensor data [21]. A recent primer advocates \"training-set engineering\", yet it offers no guidance on how labels move through the pipeline [27]. Evaluation-focused studies like [29; 30] point out benchmark staleness and emergent reasoning, yet they treat failures as downstream diagnostics rather than signals for an agent ensemble to repair its labels. Existing surveys still overlook several operational and socio-technical gaps that must be bridged before agent pipelines can be deployed safely at scale. First, robustness against adversarial or prompt-injection attacks is rarely covered, even though recent benchmarks such as PromptBench [35] reveal how small perturbations can derail LLM annotators and leak sensitive content [25]. Second, compliance with data-protection regimes is often treatedas an afterthought; yet, practical deployments must integrate safeguards like homomorphic encryption and explicit HIPAA/GDPR audit trails to prevent the disclosure of personal data [28]. Third, lifecycle maintenance is missing: contemporary reviews seldom prescribe mechanisms for continuous learning or drift detection, despite calls for HITL administrators that can halt error cascades when label quality degrades over time [26]. Fourth, the human dimension is under-examined; automation bias and job displacement threaten annotator roles, demanding new skills and interface designs that encourage critical oversight rather than rubber-stamping AI suggestions [21]. Fifth, linguistic inclusivity remains unresolved. Some evidence shows that current agent frameworks excel in high-resource languages but struggle to maintain accuracy when ported to low-resource settings [22]. Finally, few surveys quantify the computational and energy costs of privacy-enhanced or security-hardened agents, leaving practitioners without models for balancing throughput, latency, and infrastructure spend [29]. Addressing these blind spots such as robustness, governance, maintenance, labor impact, linguistic equity and cost modeling will be crucial for translating the conceptual promise of agent-based annotation into sustainable industrial practice. Building on the gaps identified above, this survey makes four specific advances. First, it presents the first complete taxonomy that stitches together manual, classic-ML and generative approaches in one coherent frame, eliminating the fragmentation seen in earlier reviews. Second, it proposes an agent-centric workflow model with planner, annotator and quality checker roles, and validates the model with concrete systems such as Self-Refine and ActiveLLM. Third, it standardizes evaluation by clustering metrics into technical, economic and user viewpoints, enabling a like-for-like comparison of throughput, cost per label and annotator trust across studies.