The transition from generative to agentic artificial intelligence has enabled autonomous systems to plan, reason, and execute complex multi-step workflows without direct human supervision. While this capability offers significant productivity gains across regulated enterprise domains—financial services, clinical operations, and software engineering—deploying these systems responsibly remains a fundamental governance challenge. Known failure modes, including hallucination propagation, goal misalignment, and cascading execution errors, can compound across long task sequences in ways that static safety architectures have struggled to contain. Traditional Human-in-the-Loop systems address these risks by inserting human approval gates at fixed checkpoints, but doing so at the cadence required for high-throughput agentic workflows imposes latency and cognitive burdens that eliminate the efficiency benefit motivating autonomous deployment in the first place. This paper proposes and empirically evaluates the Dynamic Intervention Framework, an architecture designed to resolve this tension by treating human oversight as a dynamically allocated resource rather than a fixed structural requirement. A lightweight Supervisor Agent continuously evaluates each Worker Agent decision step through a composite Contextual Confidence Score that linearly combines token-level generation probability with a semantic alignment drift measure computed via cosine similarity in the embedding space of the task goal. Decisions whose score exceeds an upper calibrated threshold execute autonomously; those in an intermediate uncertainty band execute with an asynchronous post-hoc audit flag; those falling below a lower safety threshold halt execution and escalate to a human operator through a purpose-built three-panel decision console that externalises the agent's reasoning, the proposed action, and the diagnostic rationale for interruption. A Direct Preference Optimisation feedback loop fine-tunes the Supervisor from accumulated operator correction data, progressively refining safety boundary estimates and reducing the volume of human intervention required over time. Experiments conducted on a structured dataset of 5000 synthetic enterprise automation tasks spanning financial data processing, code generation, and customer support workflows demonstrate that the Dynamic Intervention Framework achieves a task success rate of 98.2%—statistically equivalent to full human oversight at the 5% significance level—while requiring human intervention on only 14.5% of decision steps and reducing mean task latency by 89% relative to a static Human-in-the-Loop baseline. The Direct Preference Optimisation feedback mechanism reduced the intervention rate from 21.0 to 11.5% across five training epochs of 1000 tasks each, with no corresponding degradation in success rate, confirming that the reduction reflects genuine improvement in the Supervisor's safety boundary judgment rather than a loosening of oversight sensitivity. Domain-stratified analysis reveals that the framework correctly assigns higher oversight intensity to higher-risk task types, a property unavailable to uniform checkpoint models. These findings have concrete implications for the economics, regulatory compliance posture, and long-term deployment planning of enterprise agentic deployments.
KUMAR et al. (Thu,) studied this question.