🤖 AI Summary
This work addresses the challenge developers face in comprehensively evaluating the cost, code quality, and behavioral patterns of AI-assisted programming. The paper presents the first AI observability system grounded in real API invocations, integrating precise token tracking, a multi-provider LLM gateway, a pricing database covering 24 models, a response validation pipeline, and LLM-driven code review analysis. A unified dashboard enables joint insights into cost and quality, while Prometheus-based metrics ensure systematic monitoring. In six months of real-world deployment, the system maintained per-review cost estimation errors within 2% and improved the efficiency of analyzing AI usage patterns by an order of magnitude.
📝 Abstract
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.