Twinkll Sisodia

Twinkll Sisodia

The deployment of large language models (LLMs) in production environments has created an urgent need for observability systems that span the full stack -- from model internals to GPU kernels. Yet existing monitoring approaches address isolated layers of this stack, and no comprehensive analysis has examined how these techniques relate, overlap, or complement each other. This paper presents a structured analysis of five recent research contributions (2025-2026) that collectively define the emerging landscape of AI observability: confidence calibration via reinforcement learning (MIT), internal state monitoring through propositional probes (UC Berkeley), chain-of-thought monitorability evaluation (OpenAI), autonomous cloud operations benchmarking (Microsoft Research, UC Berkeley, UIUC), and non-intrusive inference-level tracing (TRUFFLD). We organize these contributions into a five-layer observability taxonomy, synthesize their key findings into a unified comparison, and identify four critical gaps that remain unaddressed. We further contextualize these research directions against practical operational observability systems that translate infrastructure telemetry into actionable insights for site reliability teams. Our analysis reveals that while individual monitoring layers have matured rapidly, the integration challenge -- connecting model-level confidence signals with infrastructure-level anomalies into coherent operational intelligence -- remains the defining open problem for the field.

Happy Bhati, Twinkll Sisodia

As AI-assisted development tools proliferate, developers face a growing challenge: understanding the cost, quality, and behavioral patterns of AI interactions across their workflow. We present a unified approach to AI observability for developer productivity tools, combining real-time token tracking, configurable model pricing registries, response validation, and cost analytics into a single-pane dashboard. Our work synthesizes two complementary systems -- Workstream, a developer productivity dashboard that centralizes pull requests, Jira tasks, and AI code reviews; and an AI observability summarizer that monitors inference workloads with Prometheus-backed metrics and multi-provider LLM gateways. We describe the architectural patterns adopted, the implementation of real token tracking from provider APIs (replacing heuristic estimation), a 24-model pricing registry, response validation pipelines, LLM-powered review intelligence, and exportable reports. Our evaluation on a six-month development workflow shows the system captures per-review cost with less than 2% variance from provider billing and reduces time-to-insight for AI usage patterns by an order of magnitude compared to manual tracking.

Twinkll Sisodia

Modern cloud-native platforms expose thousands of time series metrics through systems like Prometheus, yet formulating correct queries in domain-specific languages such as PromQL remains a significant barrier for platform engineers and site reliability teams. We present a catalog-driven framework that translates natural language questions into executable PromQL queries, bridging the gap between human intent and observability data. Our approach introduces three contributions: (1) a hybrid metrics catalog that combines a statically curated base of approximately 2,000 metrics with runtime discovery of hardware-specific signals across GPU vendors, (2) a multi-stage query pipeline with intent classification, category-aware metric routing, and multi-dimensional semantic scoring, and (3) a dynamic temporal resolution mechanism that interprets diverse natural language time expressions and maps them to appropriate PromQL duration syntax. We integrate the framework with the Model Context Protocol (MCP) to enable tool-augmented LLM interactions across multiple providers. The catalog-driven approach achieves sub-second metric discovery through pre-computed category indices, with the full pipeline completing in approximately 1.1 seconds via the catalog path. The system has been deployed on production Kubernetes clusters managing AI inference workloads, where it supports natural language querying across approximately 2,000 metrics spanning cluster health, GPU utilization, and model-serving performance.