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AgentGuard: Early-Warning and Routing for Predictable Agentic
AI on Azure

This project introduces AgentGuard, a monitoring and routing system designed to improve reliability and cost-efficiency in Azure-based agent workflows. By analysing early trajectory signals—such as reasoning patterns and tool usage—within the first 10–30% of an agent’s execution, AgentGuard predicts task outcomes and dynamically halts or re-routes agents to prevent failures. Calibrated with ADeLe for demand-aware evaluation, the system aims to optimise the Reliability–Latency–Cost frontier across diverse benchmarks, including software engineering and cybersecurity tasks. Expected outcomes include integrated Azure routing capabilities, diagnostic insights into team composition, and practical strategies for building more predictable, trustworthy agentic AI.

People

Portrait of Jiang Chen

Jiang Chen

Researcher

Universitat Politècnica de València

Portrait of David Gomez-Anton

David Gomez-Anton

Research Collaborator

Universitat Politècnica de València

Portrait of José  Hernández-Orallo

José Hernández-Orallo

Professor

Universitat Politècnica de València

Portrait of Haotian Li

Haotian Li

Researcher

Portrait of Yael Moros Daval

Yael Moros Daval

PhD Student

Universitat Politècnica de València

Portrait of Peter Romero

Peter Romero

Professor

Universitat Politècnica de València

Portrait of Daniel Romero-Alvarado

Daniel Romero-Alvarado

PhD Student

Universitat Politècnica de València

Portrait of Beibei Shi

Beibei Shi

Principal Research PM

Portrait of Xing Xie

Xing Xie

Assistant Managing Director