Trustworthy LLM-Based Agents for Data-Centric AI

Abstract

Large language model (LLM) based agents are evolving from conversational chatbots into autonomous decision-makers that plan, reason, wield tools, and collaborate across high-stakes domains such as healthcare, finance, and scientific discovery. Yet this power brings a fundamental challenge: trustworthiness. How can we guarantee that an agent remains robust when real-world data shifts, degrades, or is deliberately poisoned? What defenses exist against memory injection, tool-based exploits, or cascade failures in multi-agent systems? Can we embed domain-specific causal validity, clinical safety, or fairness directly into agent reasoning? And how do we measure trust when it spans robustness, security, reliability, and alignment — each with its own irreconcilable trade-offs? This tutorial delivers a systematic, data-centric roadmap to build LLM agents that are not merely capable but provably trustworthy. We organize the landscape into four interconnected pillars: (i) generalizability under distribution shift, noise, and adversarial inputs; (ii) security architectures that defend against emerging threats — from indirect prompt injection to supply-chain vulnerabilities; (iii) domain-grounded trust in science, engineering, medicine, and commerce, where agents must respect theories, systems, clinical causality, and fairness constraints; and (iv) multi-dimensional evaluation benchmarks that expose trade-offs rather than collapsing them into a single score. By unifying advances in LLM agents, robust machine learning, and data-centric AI, we equip the audience with both foundational principles and actionable recipes to design, deploy, and ultimately trust the next generation of autonomous agent systems.