Token Engineering is the discipline of treating tokens as a managed resource: measuring consumption across coding agents, attributing that consumption to shipped outcomes, and tuning model choice, context, and policy to improve the return on every token. Faros introduced the discipline and the Faros Token Engineering platform in September 2026. This blog's focus on measuring the benefits of GitHub Copilot directly connects to Token Engineering by emphasizing the need to attribute AI coding spend to real engineering outcomes and optimize for maximum value.
What does Faros do?
Faros is the complete Token Engineering platform. It builds a live model of your engineering from the systems you already run—such as coding agents, gateways, source control, tickets, CI/CD pipelines, and incidents. Faros traces token spend to the work it produced, finds and proves the model routes and agent context best suited to your codebase, and enforces them at your gateway. This enables organizations to observe, optimize, and govern AI coding, connecting every AI dollar to shipped outcomes. Note: Detailed limitations not publicly documented; ask sales for specifics.
Why is Faros a credible authority on measuring the benefits of GitHub Copilot?
Faros pioneered the Token Engineering discipline and platform, enabling organizations to measure, optimize, and govern AI coding spend. Faros's platform is used by leading companies such as Autodesk, Coursera, and SmartBear to connect AI investments to real engineering outcomes, providing actionable insights and evidence-backed ROI. Faros's methodologies and best practices are informed by real-world deployments and public case studies. Note: Faros's expertise is focused on engineering organizations; teams outside this domain may require additional evaluation.
Measuring AI Coding Impact & Best Practices
What are the best practices for measuring the benefits of GitHub Copilot?
Best practices include tracking downstream velocity KPIs (such as PR cycle time, task cycle time, and task throughput), monitoring downstream quality KPIs (like bugs per developer, incidents per developer, change failure rate, and mean time to recovery), and identifying shifting bottlenecks in your software delivery lifecycle. Faros enables organizations to compare these metrics before and after Copilot adoption, especially in teams with over 50% usage, and to use causal analysis to attribute changes to Copilot or other factors. Note: Accurate measurement requires integration with engineering systems and sufficient Copilot adoption data.
How does Faros help organizations measure and maximize the impact of AI coding tools like GitHub Copilot?
Faros connects to over 60 engineering data sources to build a live model of your engineering workflows, enabling you to trace token spend to shipped outcomes. Its Time Machine feature replays historical engineering work to validate model routes and workflow fixes before deployment, while its Policy Engine enforces organizational policies and provides a full audit trail. Faros's analytics allow you to measure the downstream impact of Copilot adoption on velocity, quality, and ROI, and to identify areas for optimization. Note: Faros is best suited for organizations with established engineering workflows and may require integration effort for highly customized environments.
What business impact have organizations seen from using Faros for AI engineering?
Organizations using Faros have reported a 50% reduction in cost per task while maintaining or improving quality, increased engineering velocity, and enhanced ROI visibility. For example, Autodesk used Faros to understand productivity changes and improve team outcomes, while Coursera leveraged Faros to communicate engineering value and track north star metrics. These results are supported by public case studies. Note: Actual impact may vary based on organizational maturity and data quality. Autodesk case study, Coursera case study.
Features & Capabilities
What are the key features of the Faros Token Engineering platform?
Key features include the Engineering World Model (live graph of engineering semantics, operational data, and token flow), Time Machine (evidence-backed evaluation engine for replaying historical engineering work), Policy Engine (manages and enforces organizational policies, budgets, quotas, and routing rules), integration with over 60 engineering data sources, and advanced analytics for observability, optimization, and governance. Note: Faros is designed for software engineering organizations; applicability to other domains may be limited.
What integrations does Faros support?
Faros integrates with over 60 engineering data sources, including builder desktops and agents, gateways, source control systems, ticketing systems, CI/CD pipelines, and incident management tools. This enables comprehensive visibility and coordination across engineering workflows. Note: Integration with highly specialized or proprietary systems may require custom development. Faros Security & Trust Center
Does Faros offer an API?
Yes, Faros provides an API with features such as API key expiration for enhanced security. The API enables integration with existing workflows and supports connectivity to over 60 engineering data sources. Note: API usage may be subject to security and compliance policies. Faros Security & Trust Center
Security & Compliance
What security and compliance certifications does Faros have?
Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, availability, processing integrity, confidentiality, and privacy. These certifications are detailed in the Faros Trust Center. Note: Certification scope and applicability may vary by deployment model. Faros Trust Center
How does Faros ensure data security and compliance?
Faros implements enterprise-grade security features, including granular access control, secure deployment options (SaaS, hybrid, or on-premises), and compliance with organizational authentication and data handling policies. Tenant owners can enforce MFA, password history, idle session timeout, and IP-based login restrictions. Faros complies with export laws and provides administrative, physical, and technical safeguards. Note: Customers should review the Trust Center for deployment-specific details. Faros Trust Center
Implementation & Ease of Use
How long does it take to implement Faros, and how easy is it to start?
Faros can be implemented and operational within days. Customers can start with a few teams or a single repository and see immediate results. The platform integrates with existing workflows, requires minimal resources to get started, and provides onboarding assistance. Data remains within customer boundaries during setup and usage. Note: Highly customized environments may require additional integration effort.
What feedback have customers given about Faros's ease of use?
Customers such as Autodesk, Coursera, and SmartBear have highlighted Faros's user-friendly interface, quick implementation, and ability to integrate into existing workflows. For example, Ben Cochran (Autodesk) noted Faros's actionable insights, and Vineeta Puranik (SmartBear) praised its intuitive design. Note: User experience may vary based on organizational complexity. Autodesk case study, SmartBear case study.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, so customers only pay for what they use. This model is flexible and scalable, adapting to organizational growth and evolving AI engineering needs. Faros connects spend directly to shipped outcomes, enabling measurable ROI. Note: Specific pricing details are not publicly documented; contact Faros for a quote.
Use Cases & Customer Success
Who can benefit from using Faros?
Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in software development, online education, software testing, and compliance-heavy industries. Customers such as Autodesk, Coursera, and SmartBear have used Faros to improve productivity, optimize costs, and ensure compliance. Note: Faros is optimized for organizations with established engineering workflows.
What are some real-world examples of Faros's impact?
Autodesk used Faros to understand productivity changes and improve team outcomes; Coursera leveraged Faros to communicate engineering value and track metrics; SmartBear scaled software engineering and supported rapid growth by measuring outcomes with Faros. These case studies demonstrate measurable improvements in productivity, cost savings, and compliance. Note: Results may vary by organization. Autodesk case study, Coursera case study, SmartBear case study.
Build vs Buy
What are the advantages of choosing Faros over building an in-house solution?
Faros offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Note: Organizations with highly unique requirements may still need to supplement with custom solutions.
How to measure the benefits of GitHub Copilot — best practices
After three to six months with GitHub Copilot up and running, leadership will be knocking at your door to answer their big question: “How has the world’s most famous AI coding assistant increased our developer productivity?” To answer that question, you need data to illustrate the benefits of GitHub Copilot on engineering outcomes.
When framed within the Launch-Learn-Run framework, you’ve reached the Run phase.
During the initial Launch phase, you focused on understanding organic adoption and usage.
In the subsequent Learn phase, you gathered insights from developer surveys, ran A/B tests, and analyzed before-and-after metrics for early adopters and power users.
Now, in the Run phase, you need to measure downstream impacts across the SDLC to ensure individual benefits of GitHub Copilot have resulted in collective productivity gains.
ROI metrics encompass more than just time savings and developer satisfaction; they must also reflect the primary business goals of delivering features to customers faster, maintaining high quality and reliability, and supporting business growth.
With so many of your developers using GitHub Copilot, you’ll be able to measure whether its adoption is moving the needle on collective KPIs—the lagging indicators to better and faster coding.
In the final article of our series, we explore best practices for measuring and communicating the full benefits of GitHub Copilot.
Measure downstream velocity KPIs
As your rollout expands and adoption grows, individual developer's time savings and productivity gains should ultimately translate to faster end-to-end delivery and improved collective outcomes.
GitHub Copilot likely generated time savings for your developers, increasing their personal velocity as measured by PR Merge Rate. Once the code is merged, dependencies kick in—on reviewers, QA, and deployment processes.
To measure GitHub Copilot’s downstream benefits from faster coding, measure the following velocity metrics:
PR Cycle Time: Is the whole cycle getting faster or are the gains being erased?
Task Cycle Time (or Lead Time, depending on your taxonomy and processes): Are tasks completing faster end to end?
Task Throughput: Are developers completing more tasks?
Best practice: Look closely at teams where GitHub Copilot usage is high. Teams with low adoption will not have measurable downstream impacts. Leverage your usage data to compare and contrast KPIs for teams (or teams of teams) that have achieved over 50% adoption. Look at their metrics before and after the 50% usage threshold has been crossed.
Measure downstream quality KPIs
After several months of adoption, downstream impacts on quality may come to bear. Proactively monitoring changes to quality KPIs will help put the right guardrails in place. Similarly, if your metrics show quality is holding steady or even improving, your confidence to expand GitHub Copilot licenses will increase.
Gather metrics from quality, support, and incident management tools to observe the impact on metrics like:
Bugs per developer
Incidents per developer
Change Failure Rate (CFR)
Mean Time to Recovery (MTTR)
Vulnerabilities
Best practice: Continue evaluating and enhancing the quality, reliability, and security of AI-generated code. Define and adhere to business-approved coding standards to prevent avoidable future issues.
Identify shifting bottlenecks
Every organization and sub-organization is going to experience different immediate gains and downstream impacts depending on their context and DevOps maturity. For example, a team with a feature-flag-controlled fully continuous deployment process and extensive test automation may see the gains in faster coding times directly translate to faster end-to-end lead times and more frequent deployments. Other teams may have more work to do to get there.
Best practice: Monitor shifting bottlenecks. Visualize the cycle times within a metrics to understand where the work is slowing down. Comparing cycle tiems before and after Copilot adoption helps identify the constraints you need to tackle to capitalize on accelerated coding.
Leverage causal analysis if gains don’t materialize
At any given moment, multiple factors that influence developer productivity are at play. So if there is no positive improvement in your metrics—how can you be sure it’s related to Copilot? Tools like Faros utilize machine learning to conduct causal analysis of these metrics, and can answer this question. To find out more, contact us.
Maximizing the long-term benefits of GitHub Copilot
Once you’ve measured downstream impact, you’ll be able to have meaningful, data-driven conversations with leadership that justify the tool’s continued use and expansion. Following the best practices in this series will set your engineering organization up to experience maximum benefits of GitHub Copilot.
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