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AI made software development faster, but review gaps, QA bottlenecks, and rising incident volume reveal a new risk: the Speed Trap.

Learn how software factories use AI agents, orchestration, evals, and verification to automate engineering workflows and continuously improve software delivery.

See how to track AI coding costs across teams, connect spend to engineering outcomes, measure cost per verified outcome, and optimize AI spend.

Uncover the hidden “model tax” in cheap AI coding models. Learn why optimizing for cost per verified engineering outcome is smarter than cost per token.

Why do coding agents fail? We analyzed 4,000 errors across 6 models and discovered the real culprits.

GLM 5.2 cut direct Claude Code request cost from $0.146 to $0.032 over five days. Why an image compatibility boundary still paused a broader rollout.

Which AI model is worth the spend? Benchmark cost, quality, and runtime on your own merged PRs—not on someone else’s task set. Our method and results.

New Relic’s 2026 State of AI Coding found 94% of leaders rate AI code above human code. Faros’s Acceleration Whiplash report shows what happens downstream.

The true cost of AI coding goes far beyond license fees. See how to budget the full cost of buying, running, and maintaining AI coding tools—plus the costs most models miss.

Faros proudly supports the Open Secure AI Alliance. Faros CEO, Vitaly Gordon, explains why preventing AI lock-in and utilizing open models is crucial for cybersecurity.

LLM judges give higher scores to certain coding styles, independent of whether the code works. We measured the bias, tested causes, and calibrated for it. See how we did it.

Evidence from 211 real engineering tasks shows why AI coding performance depends on the full route: model, harness, repository context, and task.

AI coding costs are exploding, but cutting access kills productivity. Faros observes, optimizes, and governs token spend so you lower cost per outcome without slowing engineers down.

More AI token spend doesn’t mean better engineering. Our on-demand webinar shows you what productive AI work actually looks like and how to close the gap between AI spend and business value.

The same flaw is hiding in your own backlog, at a higher rate. Here's why graded scoring catches what pass/fail testing misses.

The AI code evaluation framework behind our open vs. frontier model test: rubric-based scoring, a blinded LLM judge, and validation on real SWE-bench data.

Compare the best open-weight models for coding in 2026—GLM, DeepSeek, Qwen, Kimi and more—and learn when each one can cut your AI coding costs.

Learn five strategies to manage and reduce AI token costs in software development, from spend visibility to model routing to context engineering.

Claude Code analytics track usage, contribution, and cost. Learn the two ways to collect the data, where it stops, and how to connect it to engineering outcomes.

Track Claude Code token usage with built-in commands and community tools, learn what drives consumption up, and connect that spend to what your team shipped.

Open AI models matched frontier quality at half the cost in Faros's 211-task coding experiment. See the results and build your own model routing policy.

Throughput is up, quality is down, and CFOs are asking hard questions. Watch Faros CEO and a McKinsey senior partner unpack the AI engineering gap—and how to close it.

Token intelligence turns raw AI usage into operational context for engineering, finance, and leadership. Here's what it is, why it matters, and how to build it.

Finance wants to know what AI spend produced. These 3 outcome signals and 11 guardrail metrics give engineering leaders the answer.

Three outcome signals. Eleven guardrail metrics. The measurement framework for engineering leaders who need to connect token spend to shipped outcomes and know what to keep, scope, or cut.

Faros introduces Token Intelligence. Trace every AI token to the work it produced, classify spend by efficiency, and decide which tools and models to keep, scope, or cut. The first step from tokenmaxxing to outcome maxxing.

Enterprise AI token spend is surging. Learn how AI tokenomics and token intelligence help engineering leaders track, forecast, and control AI costs.

Claude Opus 4.8 hits 88.6% on SWE-bench and 0% hallucination rate on flawed data. See what else is new across agentic SWE performance, prompt injection resistance, tool use improvements, and evaluation awareness risks.

Agent = Model + Harness. Harness engineering is what makes AI agents reliable in production. See the five layers and the metrics that matter.

AI-generated code looks clean but fails beneath the surface. See what the data says about AI code quality, review burden, and how to fix it at the source.

AI coding assistant pricing changes are reshaping engineering budgets. Build a defensible ROI calculation by tool, team, and model before your next renewal.

AI tools speed up developers, but delivery feels slower. Here is what the data tells us about the gap between output and absorption.

Developer sentiment surveys track how engineers feel. But when AI coding tools cost real money and your CFO wants ROI, feelings aren't the answer. Here's what is.

Tokenmaxxing—treating AI token consumption as a productivity metric—is repeating the lines-of-code mistake. Data from 22,000 developers points to a better way to measure AI engineering impact.

Learn how a top U.S. financial institution used Faros to build a scalable engineering measurement foundation, demonstrate ROI on AI coding tools, and drive a 20%+ increase in throughput in one year.

At enterprise scale, AI engineering necessitates a connected system spanning strategy, tooling, cost management, adoption, workforce evolution, measurement, governance, and the context layer that makes AI output production-ready.

What two years of telemetry data from 22,000 developers reveals about AI's real impact on developer productivity, code quality, and business risk in 2026.

Learn how a global industrial technology leader used Faros to unify 40,000 engineers and build the measurement foundation for AI transformation.

Learn how a leading identity security provider uses Faros to power an AI-driven engineering organization and achieve a 35% increase in velocity.

Compare top AI coding models and learn how to leverage the best one by matching the model tier and level of effort to each task, using the right context.

There's a better way to measure AI productivity than counting lines of code. Focus on outcome metrics that prove business value: cycle times, quality, and delivery velocity. Learn why lines of code fails as an AI productivity metric, what outcome-based alternatives actually work, and when tracking AI code volume matters for governance and risk management.

A developer-focused look at the best AI coding agents in 2026, comparing Claude Code, Cursor, Codex, Copilot, Cline, and more—with guidance for evaluating them at enterprise scale.

Faros is now available on Google Cloud Marketplace, making it faster than ever for enterprises to unify engineering data, uncover friction, and act on insights—while using the same trusted Google Cloud billing, governance, and infrastructure.

Understand Claude Code's context window and usage limits, what really drives token costs, and how to manage AI coding spend by tying usage to engineering ROI.

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.

Research from DORA, METR, Bain, GitHub and Faros 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 helps organizations bridge the AI measurement gap to unlock faster, data-driven AI transformation.

Renewed certifications demonstrate Faros's continued commitment to keeping customer data safe.

METR's study found AI tooling slowed developers down. We found something more consequential: Developers are completing a lot more tasks with AI, but organizations aren't delivering any faster.

Key findings from the AI Productivity Paradox Report 2025. Research reveals AI coding assistants increase developer output, but not company productivity. Uncover strategies and enablers for a measurable return on investment.

See how AI agents like Claude Code can tackle tedious tech debt, from cleaning test utilities to reducing Docker image size by 50%.

Compare Claude Code vs Devin for daily development work. Learn strengths, weaknesses, and best practices from real developer experience using both AI coding tools.

Now available in Azure Marketplace for procurement with Microsoft Azure Consumption Commitment (MACC), Faros empowers enterprises to optimize engineering with AI

Learn how Vimeo’s engineering organization improved lead times, delivery metrics, and GenAI adoption with centralized visibility and insights into SDLC workflows.

Through its collaboration with Faros, Globant will introduce an innovative method for measuring and extracting insights about its team performance and impact on business results, particularly within the software development life cycle.

AI generates 25% of Google’s new code. Other organizations seek similar insights to mitigate the risks of this new age of AI-driven development.

Autodesk shares its key learnings from building an internal developer platform with an integrated visibility plane to optimize the software development lifecycle.

Discover how Riskified’s engineering organization strengthens team autonomy and accountability to achieve outstanding business results in the competitive cybersecurity market.

How we used GenAI to make querying unfamiliar data easier without letting the LLM take the wheel

Learn how SmartBear monitors portfolio investments to drive growth, improve predictability, and boost customer satisfaction

Ben Cochran, VP of Developer Enablement at Autodesk, sat down with Vitaly Gordon, Co-founder and CEO of Faros AI, at the San Francisco Engineering Leadership Council annual event, for a conversation about Autodesk’s developer productivity case study and data-driven approach to engineering.

We sat down with Mustafa Furniturewala, SVP of Engineering at Coursera, to talk about all things developer productivity.