# AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality

arXiv: https://arxiv.org/abs/2604.17092 · HTML: https://mlsanity.com/paper/2604.17092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle](https://mlsanity.com/paper/2609.04681/) (2026)

## Abstract (arXiv)

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.