[
    {
        "id": "osp-15512",
        "type": "article-journal",
        "title": "Agentic RCA for Internet-Scale Services Using Constrained Creativity",
        "author": [
            {
                "family": "Sinha",
                "given": "Sayan"
            },
            {
                "family": "Harsh",
                "given": "Vipul"
            },
            {
                "family": "Prakash",
                "given": "B. Aditya"
            },
            {
                "family": "Sekar",
                "given": "Vyas"
            },
            {
                "family": "Zhang",
                "given": "Hui"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/agentic-rca-for-internet-scale-services-using-constrained-creativity",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "System administrators of Internet-scale services need to resolve failure incidents to maintain reliability of such services. Ideally, we want a troubleshooting system to be: (1) expressive to known and unknown incidents with high accuracy; (2) cost efficient at scale; (3) explainable to provide actionable insights operators can act on; and (4) entail low effort from the operators. Unfortunately, most existing systems, including emerging LLM-assisted agentic workflows and structured frameworks for authoring diverse RCA algorithms fall short of achieving all four requirements. We present E4, a novel agentic system for troubleshooting for Internet-scale services. E4 embodies the paradigm of constrained creativity that combines the best of LLM-assisted automation and exploration with the explainability and efficiency of a structured approach. Instead of allowing an LLM agent to write arbitrary code or generate arbitrary responses, we provide the agent a restricted DSL to generate its response via simple loop-free data flow programs. This DSL, equipped with high level operators for troubleshooting, makes E4's output accurate, verifiable and explainable. On a mix of synthetic and real-world workloads, E4 achieves up to 62% better accuracy compared to state-of-the-art solutions, while providing more explainable responses at up to 12x reduced cost."
    }
]