Building AI Agents for Autonomous Clouds: Challenges and Design Principles

The rapid growth in the use of Large Language Models (LLMs) and AI Agents as part of software development and deployment is revolutionizing the information technology landscape. While code generation receives significant attention, a higher-impact application lies in using AI agents for operational resilience of cloud services, which currently require significant human effort and domain knowledge. There is a growing interest in AI for IT Operations (AIOps) which aims to automate complex operational tasks, like fault localization and root cause analysis, thereby reducing human intervention and customer impact. However, achieving the vision of autonomous and self-healing clouds though AIOps is hampered by the lack of standardized frameworks for building, evaluating, and improving AIOps agents. This vision paper lays the groundwork for such a framework by first framing the requirements and then discussing design decisions that satisfy them. We also propose AIOpsLab, a prototype implementation leveraging agent-cloud-interface that orchestrates an application, injects real-time faults using chaos engineering, and interfaces with an agent to localize and resolve the faults. We report promising results and lay the groundwork to build a modular and robust framework for building, evaluating, and improving agents for autonomous clouds.

Configuration Validation with Large Language Models

Misconfigurations are the major causes of software failures. Existing configuration validation techniques rely on manually written rules or test cases, which are expensive to implement and maintain, and are hard to be comprehensive. Leveraging machine learning (ML) and natural language processing (NLP) for configuration validation is considered a promising direction, but has been facing challenges such as the need of not only large-scale configuration data, but also system-specific features and models which are hard to generalize. Recent advances in Large Language Models (LLMs) show the promises to address some of the long-lasting limitations of ML/NLP-based configuration validation techniques. In this paper, we present an exploratory analysis on the feasibility and effectiveness of using LLMs like GPT and Codex for configuration validation. Specifically, we take a first step to empirically evaluate LLMs as configuration validators without additional fine-tuning or code generation. We develop a generic LLM-based validation framework, named Ciri, which integrates different LLMs. Ciri devises effective prompt engineering with few-shot learning based on both valid configuration and misconfiguration data. Ciri also validates and aggregates the outputs of LLMs to generate validation results, coping with known hallucination and nondeterminism of LLMs. We evaluate the validation effectiveness of Ciri on five popular LLMs using configuration data of six mature, widely deployed open-source systems. Our analysis (1) confirms the potential of using LLMs for configuration validation, (2) understands the design space of LLMbased validators like Ciri, especially in terms of prompt engineering with few-shot learning, and (3) reveals open challenges such as ineffectiveness in detecting certain types of misconfigurations and biases to popular configuration parameters.