AI agents often fail in ways that are difficult to localize because executions are probabilistic, long-horizon, multi-agent, and mediated by noisy tool outputs. We address this gap by manually annotating failed agent runs and release a novel benchmark of 170 trajectories across 11 diverse task settings, including structured API workflows, incident management, and open-ended web/file tasks. Each trajectory is annotated with a critical failure step and a category from a grounded-theory derived, cross-domain failure taxonomy. To mitigate the human cost of failure attribution, we present AgentRx, an automated diagnostic framework that pinpoints the critical failure step in a failed agent trajectory. It synthesizes constraints, evaluates them step-by-step, and produces an auditable validation log of constraint violations with associated evidence; an LLM-based judge uses this log to localize the critical step and category. AgentRx improves step localization by 75% on average over prior work, while providing failure category attribution.

Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations: textual workflow skills provide natural language guidance but cannot be directly executed, while code-based skills execute without giving the agent step-level guidance for adaptation or recovery. We introduce WebXSkill, a framework that bridges this gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance. WebXSkill operates in three stages: skill extraction mines reusable action subsequences from readily available synthetic agent trajectories and abstracts them into parameterized skills, skill organization indexes them into a URL-based graph for context-aware retrieval, and skill deployment exposes two complementary modes, grounded mode for fully automated execution and guided mode where skills serve as step-by-step instructions the agent follows with its native planning. WebXSkill demonstrates consistent improvements on WebArena, WebVoyager, and Online-Mind2Web. We further find that better skill deployment mode depends on a model’s plan and execution capability. The code is available at https://github.com/aiming-lab/WebXSkill.

Long-horizon personalization requires dialog assistants to retrieve user-specific facts from extended interaction histories. However, many relevant facts have low semantic similarity to the query under dense retrieval. Standard Retrieval-Augmented Generation (RAG) and GraphRAG systems remain largely retrospective: they rely on embedding similarity or fixed graph traversals, often missing relevant facts that lie far from the query in embedding space. Inspired by prospection, the human ability to use imagined futures as cues for recall, we introduce Prospection-Guided Retrieval (PGR), which decouples retrieval from memory storage. Given a user query, PGR expands the goal into a linear chain or tree of thoughts (TOT) denoting plausible next steps, and uses these steps as retrieval probes instead of the original query alone. Retrieved facts are then used to personalize the next round of prospection, enabling PGR to uncover additional memories by grounding the simulation in the user’s history. We also introduce MemoryQuest, a challenging multi-session benchmark where each query is annotated with 3–5 dated reference facts under a low query-reference similarity constraint. Across 1,625 queries spanning 185 user profiles from 3 public datasets, PGR-TOT substantially improves retrieval, achieving nearly 3x recall on MemoryQuest over baselines. In pairwise LLM-as-judge evaluations, PGR-generated responses are preferred on 89–98% of queries, with blinded human annotations showing a similar trend. Overall, the results show that explicit prospection yields large gains in long-horizon retrieval and response quality over similarity-only baselines.