AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915943710457856 |
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| author | Bhati, Happy Sisodia, Twinkll |
| author_facet | Bhati, Happy Sisodia, Twinkll |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_17092 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality Bhati, Happy Sisodia, Twinkll Software Engineering D.2.6; K.6.3 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. |
| title | AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality |
| topic | Software Engineering D.2.6; K.6.3 |
| url | https://arxiv.org/abs/2604.17092 |