Institution profile

Universidad Torcuato Di Tella

Academic institutionnorthamerica · us
Official website
Research library14linked papers
Opportunities0open roles
Selected work

Representative Papers

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation

Jul 28, 2026

This work addresses silent decision errors in AI agents caused by metadata defects—such as stale prices or superseded records—that evade model detection and conventional data quality alerts. The authors propose a runtime data quality gating mechanism that identifies such defects prior to action execution and integrates downstream-only repair strategies to prevent high-cost errors stemming from unreliable evidence. Innovatively treating evidence integrity as a system-level dimension orthogonal to model capability, they design a model-free oracle based on task-decision geometry to analyze defect propagation patterns. Experimental results demonstrate full recovery of performance loss within the method’s coverage scope, with the oracle achieving a mean absolute error of 0.015 (Pearson r = 0.876), empirically validating the “flat-staircase” phenomenon wherein defect impact remains independent of model proficiency.

0 citationsRead paper

Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems

Jun 14, 2026

This work addresses the absence of real-time cost and carbon emission constraints in existing AI agent systems, which typically rely on post-hoc monitoring. The authors propose the first verifiable, zero-overrun budget and carbon gating mechanism embedded directly within the agent execution loop, grounded in the SARC governance framework and enforcing predictive governance at four critical decision points. Their approach integrates quantile conformal calibration, soft Lagrangian penalties, and both synthetic and real task arrival models, and they release a complete open-source governance library. Experiments on real-world multi-step tasks demonstrate zero budget overruns and achieve end-to-end reductions of 47–55% in token usage, monetary cost, and carbon emissions, with all results fully reproducible.

0 citationsRead paper

SARC: A Governance-by-Architecture Framework for Agentic AI Systems

May 08, 2026

Current AI agent systems lack effective runtime governance over tool usage and multi-agent behaviors, leading to a disconnect between compliance requirements and actual execution. This work proposes the SARC framework, which formalizes governance constraints as executable specifications—comprising source, category, predicate, and validation point—and embeds them as first-class objects within the agent’s operational loop. SARC enforces these constraints through a four-tier mechanism: pre-action gating, in-action monitoring, post-action auditing, and escalation routing, enabling real-time verification and enforcement before, during, and after actions, as well as along escalation pathways. In procurement task evaluations, SARC achieved zero violations of hard constraints and reduced soft constraint exceedances by 89.5%, with residual violations primarily attributable to execution stack errors rather than environmental risks, thereby demonstrating its effectiveness and traceability.

0 citationsRead paper
Recent publications

Latest Papers

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation

Jul 28, 2026

This work addresses silent decision errors in AI agents caused by metadata defects—such as stale prices or superseded records—that evade model detection and conventional data quality alerts. The authors propose a runtime data quality gating mechanism that identifies such defects prior to action execution and integrates downstream-only repair strategies to prevent high-cost errors stemming from unreliable evidence. Innovatively treating evidence integrity as a system-level dimension orthogonal to model capability, they design a model-free oracle based on task-decision geometry to analyze defect propagation patterns. Experimental results demonstrate full recovery of performance loss within the method’s coverage scope, with the oracle achieving a mean absolute error of 0.015 (Pearson r = 0.876), empirically validating the “flat-staircase” phenomenon wherein defect impact remains independent of model proficiency.

0 citationsRead paper

Green SARC: Predictive Cost and Carbon Governance for Agentic AI Systems

Jun 14, 2026

This work addresses the absence of real-time cost and carbon emission constraints in existing AI agent systems, which typically rely on post-hoc monitoring. The authors propose the first verifiable, zero-overrun budget and carbon gating mechanism embedded directly within the agent execution loop, grounded in the SARC governance framework and enforcing predictive governance at four critical decision points. Their approach integrates quantile conformal calibration, soft Lagrangian penalties, and both synthetic and real task arrival models, and they release a complete open-source governance library. Experiments on real-world multi-step tasks demonstrate zero budget overruns and achieve end-to-end reductions of 47–55% in token usage, monetary cost, and carbon emissions, with all results fully reproducible.

0 citationsRead paper

SARC: A Governance-by-Architecture Framework for Agentic AI Systems

May 08, 2026

Current AI agent systems lack effective runtime governance over tool usage and multi-agent behaviors, leading to a disconnect between compliance requirements and actual execution. This work proposes the SARC framework, which formalizes governance constraints as executable specifications—comprising source, category, predicate, and validation point—and embeds them as first-class objects within the agent’s operational loop. SARC enforces these constraints through a four-tier mechanism: pre-action gating, in-action monitoring, post-action auditing, and escalation routing, enabling real-time verification and enforcement before, during, and after actions, as well as along escalation pathways. In procurement task evaluations, SARC achieved zero violations of hard constraints and reduced soft constraint exceedances by 89.5%, with residual violations primarily attributable to execution stack errors rather than environmental risks, thereby demonstrating its effectiveness and traceability.

0 citationsRead paper