Trustworthy Agentic AI Through Dynamic Task Allocation, Uncertainty Estimation, and Collaborative Verification
Keywords:
agentic AI, dynamic task allocation, uncertainty estimation, collaborative verification, AI governance, multi-agent systems, trustworthy systems, human oversightAbstract
Agentic artificial intelligence increasingly moves beyond passive prediction toward sustained autonomy, tool use, and multi-step coordination across socio-technical environments. This shift creates a trust problem that cannot be resolved solely through model scaling or post hoc alignment. The paper examines trustworthy agentic AI through three interconnected system-level mechanisms: dynamic task allocation, uncertainty estimation, and collaborative verification. Dynamic task allocation distributes responsibilities among heterogeneous human and machine actors according to competence, context, risk, and evolving operational conditions. Uncertainty estimation provides a continuous signal about confidence, ambiguity, distributional shift, and model limitations, enabling agents to defer, request help, or narrow their scope. Collaborative verification introduces redundancy and adversarial scrutiny through peer agents, human reviewers, institutional auditors, and cryptographic attestations. Together, these mechanisms form a layered architecture for reliability, accountability, fairness, and robustness. The analysis emphasizes structural trade-offs involving latency, scalability, privacy, incentive design, and governance. It also considers deployment constraints in healthcare, transportation, finance, public administration, and scientific discovery. The central argument is that trustworthy agentic systems require dynamic control planes rather than static policies, because the conditions that determine safe action change during operation. Trust emerges not as a property of a single model but as an outcome of institutional design, infrastructure, verification workflows, and continuous oversight. The paper concludes with implications for policy, engineering practice, and future research on sustainable and contestable agentic infrastructures.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.