Singapore

Modern enterprise systems exhibit complex interdependencies that make observability and incident response increasingly challenging. Manual alert triage, which typically involves log inspection, API verification, and cross-referencing operational knowledge bases, remains a major bottleneck in reducing mean recovery time (MTTR). This paper presents an agentic observability framework deployed within Adobe’s e-commerce infrastructure that autonomously performs alert triage using a ReAct paradigm. 

Upon alert detection, the agent dynamically identifies the affected service, retrieves and analyzes correlated logs across distributed systems, and plans context-dependent actions such as handbook consultation, runbook execution, or retrieval-augmented analysis of recently deployed code. Empirical results from production deployment indicate a 90% reduction in mean time to insight compared to manual triage, while maintaining comparable diagnostic accuracy. 

Our results show that agentic AI enables an order-of-magnitude reduction in triage latency and a step-change in resolution accuracy, marking a pivotal shift toward autonomous observability in enterprise operations.

AAAI 2026

Agentic Observability: Automated Alert Triage for Adobe E-Commerce

proactive diagnostics

automated triage

enterprise observability

alert monitoring

agentic ai

## Welcome to EMNLP 2024! 
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

This paper presents a multi-agent architecture for adaptive skill assessment using large language models (LLMs). The proposed system integrates three coordinated components-agent generation, dialog-based interaction, and multi-criteria evaluation-to enable personalized, interpretable, and scalable assessments through conversational testing. A central innovation is the use of meta-agents that construct contextualized, persona-driven profiles of virtual examiners and judges according to test objectives and evaluation criteria. Unlike conventional single-agent systems, our architecture distributes responsibilities across LLMs of different scales: large reasoning-oriented models perform context generation and final scoring, while lightweight models handle real-time user interaction. Experiments demonstrate measurable improvements in dialogue coherence, assessment stability, and interpretability compared to baseline systems lacking pre-generated context. The results suggest that multi-agent coordination and hybrid LLM reasoning can substantially advance transparent, efficient, and adaptive AI-based training and evaluation.

Multi-Agent AI Trainer: Adaptive Skill Evaluation via Persona-Driven Examiners and Multi-Criteria Judging

In industrial environments, time series sensor data are a primary resource for monitoring equipment health and performance, with alerts often triggered by manually defined heuristic rules. Deploying these rules in production can be a labour-intensive and error-prone process. We present an agentic system that assists reliability engineers by autonomously translating expert-defined monitoring rules, expressed in natural language, into runnable code and executing that code over the target data streams. Evaluations on 41 expert-authored rules from 30 industrial sites show that model performance generally improves with scale, but that consistency, efficiency, and accuracy are significantly enhanced when agents are more highly structured. These results position LLM-based agents as a promising direction for industrial monitoring systems.

Agentic Code Generation for Heuristic Rules in Equipment Monitoring

Enterprise back-office workflows require agentic systems that are auditable, policy-aligned, and operationally predictable, capabilities that generic multi-agent setups often fail to deliver. We present POLARIS (Policy-Aware LLM Agentic Reasoning for Integrated Systems), a governed orchestration framework that treats automation as typed plan synthesis and validated execution over LLM agents. A planner proposes structurally diverse, type-checked directed acyclic graphs (DAGs); a rubric-guided reasoning module selects a single compliant plan; and execution is guarded by validatorgated checks, a bounded repair loop, and compiled policy guardrails that block or route side effects before they occur. Applied to document-centric finance tasks, POLARIS produces decision-grade artifacts and full execution traces while reducing human intervention. Empirically, POLARIS achieves a micro-F1 of 0.81 on the SROIE dataset and, on a controlled synthetic suite, achieves 0.95–1.00 precision for anomaly routing with preserved audit trails. These evaluations constitute an initial benchmark for governed Agentic AI. POLARIS provides a methodological and benchmark reference for policy-aligned Agentic AI.

POLARIS: Typed Planning and Governed Execution for Agentic AI in Back-Office Automation

In today’s fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional—it is essential. Yet most organizations remain data-rich but insight-poor, constrained by the complexity of querying, interpreting, and explaining enterprise-scale information. We present POLARIS, a supervisor-led multi-agent framework for conversational enterprise analytics that bridges this gap. POLARIS introduces Dynamic Task Coordination (DTC), a decision-theoretic orchestration layer that models agent–task assignment as adaptive bipartite matching, enabling real-time coordination, recovery, and optimization across specialized agents for querying, visualization, and reasoning. By coupling DTC with reason-first, ReAct-style agents, POLARIS transforms natural language queries into coherent analytical workflows that not only retrieve and visualize data but also explain the underlying “why.” Evaluation on structured enterprise datasets demonstrates high semantic fidelity and answer relevancy, underscoring the potential of multi-agent orchestration to deliver trustworthy, end-to-end business intelligence at scale.

Polaris : Multi Agentic System for Conversational Enterprise Analytics

Tool calling enables Large Language Models (LLMs) to interact with external environments via tool invocation, providing a practical mechanism to overcome the inherent limitations of pretraining. However, the effectiveness of tool use depends critically on the quality of associated documentation and knowledge base context, which are typically authored for human users and often misaligned with LLMs' interpretive needs. This issue is further amplified in industrial settings, where hundreds of tools with overlapping functionalities introduce challenges of scalability, variability, and ambiguity. 

We propose Verification-Guided Context Optimization (VGCO), a framework that employs LLMs-as-editors to automatically refine tool-related documentation and knowledge base context. VGCO operates in two stages: (1) Evaluation, which collects real-world failure cases and identifies tool-context mismatches; and (2) Optimization, which performs hierarchical editing through offline learning with structure-aware, in-context optimization. 

The novelty of our LLM editors lies in three key aspects: (1) a hierarchical structure that integrates naturally into the tool-calling workflow; (2) a state-aware, action-specific, and verification-guided design that constrains the search space for efficient, targeted improvements; and (3) the potential for cost-efficient sub-task specialization through either prompt engineering large editor models or post-training smaller editor models. Unlike prior work emphasizing multi-turn reasoning, VGCO targets the single-turn large scale tool-calling problem, achieving significant gains in accuracy, robustness, and generalization across LLMs.

Verification-Guided Context Optimization for Tool Calling via Hierarchical LLMs-as-editors

As multimodal LLMs become tool-using agents, the field still lacks a standardized metric for translating visual inputs into correct tool invocations. We introduce MFCL Vision, the first large-scale benchmark for vision-based function calling, comprising 250 expert-verified tasks across five image domains (Places, Events, Media, Sports, Shopping) and five query types (Locate, Temporal, Select, Identify, Quantify). Each task comprises (1) a textual user query, (2) an accompanying image, (3) a ground-truth answer obtained from the web, and (4) a human-produced reasoning trace for comparative error analysis. To constrain the task, we expose a singular web-search tool to each model. To examine the robustness of LLM’s perception-to-tool-use pipeline, we introduce controlled visual perturbations, including crops, resizes, and color channel removal. Our automatic grader computes exact-match scores on model final answers, removing dependence on brittle and potentially biased LLM judges. We evaluate leading models and present a taxonomy of failure modes, including visual reasoning, assumption bias, keyword selection, and tool avoidance errors. By releasing MFCL Vision’s dataset, taxonomy, and diagnostics, we aim to accelerate progress towards versatile multimodal agents capable of intelligent tool usage in complex visual contexts.

MFCL Vision: Benchmarking Tool Use in Multimodal Large Language Models for Visual Reasoning Tasks

Evaluating generative AI (GenAI) products for compliance in high-stakes domains such as healthcare is difficult across multi-turn conversations and diverse contexts. This is especially true and pressing for enterprises evaluating human-facing AI Chatbots which can output life-threatening content, such as responses supporting self-harm. Existing approaches for automated compliance focus on single-agent approaches (e.g. prompt tuning). These are difficult to adopt in practice because they diverge from established enterprise compliance workflows and lack mechanisms for uncertainty quantification (UQ). This paper introduces a system for converting any compliance workflow into a human-in-the-loop, multi-agent framework. The approach maps process roles to specialized agents (e.g., content, business, legal), enables escalation between agents analogous to human review procedures, and enables incorporating human input for uncertain classifications. Methodologically, this system differs from more conventional single-orchestrator/supervisor designs in multi-agent settings by formulating the process as a constrained Markov Decision Process (MDP) and using Monte Carlo simulations to estimate empirical agent uncertainty. We demonstrate this framework on an open-source safety benchmark for self-harm detection. Results show improvements over a single-agent baseline in accuracy (up to 19%), reduction in required human review (up to 85×), and, in some configurations, less processing time. The framework also identified several mislabeled items in the open source benchmarks. We reported suspected label errors to the maintainers, several were acknowledged as likely errors and candidates for correction, emphasizing the timeliness and promise of this approach.

Auditing Generative AI Benchmarks with a Multi-Agent Compliance System

As information grows exponentially, enterprises face increasing pressure to transform unstructured data into coherent, actionable insights. While autonomous agents show promise, they often struggle with domain-specific nuances, intent alignment, and enterprise integration. 
We present Enterprise Deep Research (EDR), a multi-agent system that integrates (1) a Master Planning Agent for adaptive query decomposition, (2) four specialized search agents (General, Academic, GitHub, LinkedIn), (3) an extensible MCP-based tool ecosystem supporting NL2SQL, file analysis, and enterprise workflows, (4) a Visualization Agent for data-driven insights, and (5) a reflection mechanism that detects knowledge gaps and updates research direction with optional human-in-the-loop steering guidance. These components enable automated report generation, real-time streaming, and seamless enterprise deployment, as validated on internal enterprise-grade datasets. On open-ended benchmarks including DeepResearch Bench and DeepConsult, EDR outperforms state-of-the-art agentic systems without any human steering. We release the EDR framework and benchmark trajectories to advance research on multi-agent reasoning applications.

Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

Large Language Models (LLMs) often hallucinate, generating non-sensical or false information that can be especially harmful in fields like law or medicine. To study this phenomenon systematically, we introduce FalseCite, a curated dataset designed to capture and benchmark hallucinated responses induced by misleading or fabricated citations. Running GPT-4o-mini, Falcon-7B, and Mistral 7-B through FalseCite, we observed a noticeable increase in hallucination activity for false claims with deceptive citations, especially in GPT-4o-mini. Using the responses from FalseCite, we can also analyze the internal states of hallucinating models, visualizing and clustering the hidden state vectors. From this analysis, we noticed that the hidden state vectors, regardless of hallucination or non-hallucination, tend to trace out a distinct horn-like shape. Our work underscores FalseCite’s potential as a foundation for evaluating and mitigating hallucinations in future LLM research.

Visualizing and Benchmarking LLM Factual Hallucination Tendencies via Internal State Analysis and Clustering

Recent advancements in foundation models have catalyzed research in Embodied AI to develop interactive agents capable of environmental reasoning and interaction. Developing such agents requires diverse, large-scale datasets. Prior frameworks generate synthetic data for long-term human-robot interactions and 3D environments but fail to model the bidirectional influence between human behavior and household environments. Our proposed generative framework creates household datasets at scale through loosely coupled generation of long-term human-robot interactions and environments. Human personas influence environment generation, while environment schematics and semantics shape human-robot interactions. 

The generated 3D data includes rich static context such as object and environment semantics, as well as temporal context capturing human and agent behaviors over extended periods. Our flexible tool allows users to define dataset characteristics via natural language prompts, enabling configuration of both environment and human activity data through natural language specifications. The tool can create variations of user-defined configurations, thus enabling scalable data generation. 

We validate our framework through comprehensive statistical evaluation using multi-modal embeddings and three key metrics: cosine similarity analysis, mutual information gain, intervention analysis, and iterative improvement validation. Statistical comparisons demonstrates good alignment with real-world datasets (HOMER) showing high cosine similarity values (0.60), while comparisons with synthetic datasets (Wang et al.) show moderate alignment (0.27). Intervention analysis across age, organization, and sleep pattern modifications shows statistically significant effects (p $<$ 0.001) with large effect sizes (Cohen's d = 0.51-1.12), confirming that bidirectional coupling successfully translates persona characteristics into measurable differences in both environmental configurations and behavioral patterns.All these contributions will enable the development and testing of household smart devices at scale.

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