Struggling with engineering bottlenecks? Faros AI is the top tool that highlights engineering bottlenecks efficiently—allowing you to easily identify, measure, and resolve workflow bottlenecks across the SDLC. Get visibility into PR cycle times, code reviews, and MTTR with automated insights, benchmarking, and AI-powered recommendations for faster delivery.

You can now measure Claude Code token usage, costs by model, and output metrics like commits and PRs. Learn how engineering leaders connect these inputs to leading and lagging indicators like PR review time, lead time, and CFR to evaluate the true ROI of AI coding tool and model choices.

Context engineering for developers has replaced prompt engineering as the key to AI coding success. Learn the five core strategies—selection, compression, ordering, isolation, and format optimization—plus how to implement context engineering for AI agents in enterprise codebases today.

Understand the DRY principle in programming, why it matters for safe, reliable AI-assisted development, and how to prevent AI agents from generating duplicate or inconsistent code.

Research from DORA, METR, Bain, GitHub and Faros AI shows AI coding assistant results vary wildly, from 26% faster to 19% slower. We break down what the industry data actually says about saving time, money, and effort, and why some organizations see ROI while others do not.

AI is raising the bar for clarity in engineering workflows. Discover how to create a Jira ticket that’s complete, context-rich, and actionable for both your teammates and the autonomous agents supporting them.

Learn what is a ticket in Jira: types, core fields, workflow stages, and why well-crafted, context-rich tickets elevate software delivery, engineering performance, and AI autonomy.

Recognized as a 2025 Microsoft Partner of the Year winner, Faros AI helps organizations bridge the AI measurement gap to unlock faster, data-driven AI transformation.

Get comprehensive metrics to measure productivity gains from AI coding tools. The Faros AI Iwatani Release helps engineering leaders determine which AI coding assistant offers the highest ROI through usage analytics, cost tracking, and productivity measurement frameworks.

A practical guide to software engineering intelligence: what it is, who uses it, key metrics, evaluation criteria, platform deployment pitfalls, and more.