Akila Balasubramanian

From Signals to Root Cause: A Systems Architecture for Agentic AI in Observability

Abstract : 

Modern distributed systems generate high-cardinality telemetry across metrics, logs, and traces, creating a combinatorial search space that renders manual root cause analysis (RCA) increasingly impractical at cloud scale. Existing approaches—including rule-based automation and prompt-driven large language model (LLM) systems—fail to support reliable RCA due to the absence of structured multi-step reasoning, persistent state management, and deterministic execution. This paper presents an agentic systems framework that models RCA as a closed-loop, sequential decision-making process over observability telemetry. A layered architecture is introduced comprising a control layer for state-machine-based orchestration, a memory layer for token-aware context management, a tooling layer for deterministic interaction with heterogeneous observability backends, and a governance layer for enforcing correctness, security, and auditability. RCA is executed through iterative hypothesis refinement, supported by algorithms for action selection, evidence aggregation, conflict resolution, and failure recovery. Empirical evaluation across 1,200 production-style troubleshooting tasks demonstrates that the proposed system improves task success rates from 61.8% to 86.7%, reduces user intervention by 3.5×, decreases effective time-to-resolution by approximately 42%, and reduces token consumption by up to 4.8× through adaptive memory strategies. Robustness experiments show nearly 2× improvement in failure recovery and significant gains in handling ambiguous inputs compared to prompt-only and static pipeline baselines. These results establish that agentic architectures can transform observability from passive telemetry monitoring into active, evidence-driven, automated reasoning.

Profile:

Akila Balasubramanian is a Software Engineering Technical Leader at Cisco, where she leads the design and development of AI-powered observability and intelligent troubleshooting capabilities for cloud-native platforms. Her work spans distributed systems, agentic AI, observability, and platform engineering, with a focus on building production-ready AI systems that help engineering teams detect, investigate, and resolve complex operational issues more effectively.

Over the years, Akila has led the development of technologies including AI-directed troubleshooting, automated root cause analysis, real user monitoring, session replay, telemetry intelligence, and intelligent investigation workflows. Her work emphasizes trustworthy AI, explainable reasoning, robust evaluation, and scalable platform architecture that can support enterprise-scale production environments.

Akila is passionate about bridging research and real-world engineering, translating advances in AI into reliable systems that solve meaningful customer problems. She frequently shares practical lessons on building agentic AI platforms, LLM evaluation, observability, and the engineering challenges of deploying intelligent systems at scale.