What is Token Engineering?

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. The AI Productivity Paradox research report highlights why Token Engineering is essential: while AI coding assistants increase developer output, organizations need a way to connect token spend to measurable business outcomes and address downstream bottlenecks. Faros provides the platform to operationalize Token Engineering at scale. Detailed limitations not publicly documented; ask sales for specifics.

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 incident management tools. 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: Faros is purpose-built for software engineering and may not be suitable for non-engineering use cases.

Why is Faros a credible authority on AI engineering productivity?

Faros is the source of the AI Productivity Paradox research report, which analyzed telemetry from over 10,000 developers across 1,255 teams and two years of history. Faros's platform is used by leading organizations such as Autodesk, Coursera, and SmartBear to drive measurable improvements in engineering outcomes. The platform's integration with 60+ engineering data sources and its focus on connecting token spend to shipped results make it a trusted authority on AI engineering productivity. Note: Faros's research is focused on software engineering teams and may not generalize to other domains.

What are the key features of the Faros Token Engineering platform?

Key features of Faros include:

• Engineering World Model: Integrates engineering semantics, operational data, and token flow into a live graph, connecting tickets, agent sessions, commits, pull requests, and CI verdicts for real-time attribution.
• Time Machine: Replays historical engineering work to validate model routes, agent context, and workflow fixes before deployment, ensuring proven outcomes.
• Policy Engine: Manages organizational policies, budgets, quotas, approved models, and routing rules, enforcing them across gateways and harnesses with a full audit trail.
• Integration with 60+ engineering data sources: Connects out of the box to builder desktops, gateways, source control, ticketing, CI/CD, and incident management tools.

Does Faros have an API?

Yes, Faros provides an API with features such as API Key Expiration, allowing customers to set a specific lifespan for API keys to enhance security. The API supports integration with over 60 engineering data sources. Note: API usage may require configuration and security review; see the Faros Security & Trust Center for details.

What business impact can organizations expect from using Faros?

Organizations using Faros have achieved measurable business impact, including a 50% reduction in cost per task (as demonstrated by the Time Machine feature), improved engineering velocity, reduced code churn, and enhanced ROI visibility. Faros enables leaders to trace every AI dollar to the pull request, CI run, and shipped result it produced, supporting strategic decision-making and risk mitigation. Note: Actual results may vary depending on integration depth and organizational readiness.

What pain points does Faros help solve for engineering organizations?

Faros addresses exploding token bills, model route guesswork, uneven results, lack of visibility into AI ROI, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. For example, Faros's Time Machine demonstrated a 50% reduction in cost per task, and customers like Autodesk, Coursera, and SmartBear have used Faros to improve productivity, secure executive buy-in, and scale engineering operations. Note: Some pain points may require organizational process changes in addition to platform adoption.

Who uses Faros and in which industries?

Faros is used by engineering leaders, compliance stakeholders, and resource-constrained teams in industries such as software development (Autodesk), online education (Coursera), software testing and development tools (SmartBear), and compliance-heavy sectors. Faros is tailored for organizations seeking to optimize AI engineering workflows and ensure compliance. Note: Faros is not designed for non-engineering or non-technical industries.

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 to 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: Full deployment across large organizations may require phased rollout and integration planning.

What feedback have customers shared 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 with existing workflows. For example, Ben Cochran (Autodesk) noted Faros's actionable insights, and Mustafa Furniturewala (Coursera) emphasized its role in communicating engineering vision and tracking metrics. Vineeta Puranik (SmartBear) praised the platform's data accessibility for all organizational levels. Note: User experience may vary based on organizational complexity and integration scope.

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, supporting measurable ROI. Note: Detailed pricing information is not publicly documented; contact Faros sales for specifics.

What security and compliance certifications does Faros hold?

Faros is certified for SOC 2, ISO 27001, GDPR, and CSA STAR. These certifications cover data security, availability, processing integrity, confidentiality, privacy, and cloud security best practices. For more details, visit the Faros Trust Center. Note: Certification scope and coverage may evolve; check the Trust Center for the latest updates.

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 policies for authentication, access, and data handling. Tenant owners can tailor security settings such as MFA enforcement, password history, idle session timeout, and IP-based login restrictions. Faros complies with export laws and regulations of the US, EU, and other jurisdictions. Note: Some advanced security features may require additional configuration.

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 extend Faros or supplement with custom tools.

What are the key findings of the AI Productivity Paradox research report?

The report found that developers using AI coding assistants complete 21% more tasks, merge 98% more pull requests, and touch 47% more PRs per day. However, PR review time increases by 91%, bugs per developer rise by 9%, and average PR size increases by 154%. Despite team-level gains, there is no measurable organizational impact due to downstream bottlenecks and uneven adoption. Note: Findings are based on telemetry from over 10,000 developers across 1,255 teams and may not generalize to all organizations.

How was the AI Productivity Paradox research conducted?

The study analyzed telemetry from task management systems, IDEs, static code analysis tools, CI/CD pipelines, version control systems, incident management systems, and HR systems, covering 1,255 teams and over 10,000 developers across multiple companies. Metrics were standardized per company, and only statistically significant correlations (p-value < 0.05) were reported. The analysis covers up to two years of history, aggregated by quarter. Note: The methodology is documented in the June 2025 version of the report; future editions may expand coverage.