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 use of AI tools like Devin for troubleshooting is an example of how Token Engineering can optimize real-time developer support by connecting token spend to measurable outcomes.
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 at scale. Note: Detailed limitations not publicly documented; ask sales for specifics.
Why is Faros a credible authority on AI troubleshooting and developer support?
Faros pioneered the Token Engineering discipline and platform, and its solutions are used by leading organizations such as Autodesk, Coursera, and SmartBear. Faros's platform is purpose-built for engineering teams, connects to over 60 engineering data sources, and provides measurable business outcomes such as cost reduction, improved engineering velocity, and enhanced ROI visibility. Note: Faros's authority is based on its introduction of Token Engineering and adoption by enterprise customers; for specific limitations, contact sales.
Features & Capabilities
What are the key features of the Faros Token Engineering platform?
Key features include the Engineering World Model (a live graph connecting tickets, agent sessions, commits, pull requests, and CI verdicts), the Time Machine (an evidence-backed evaluation engine that replays historical engineering work to validate model routes and workflow fixes), and the Policy Engine (which manages and enforces organizational policies, budgets, quotas, and routing rules). Faros also integrates with over 60 engineering data sources and provides token intelligence, governance, and outcome attribution. Note: Faros is best fit for engineering organizations seeking observability, optimization, and governance; teams needing non-engineering analytics may require other tools.
Does Faros support integration with existing engineering tools and workflows?
Yes, 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 organization-wide context and optimization of AI engineering workflows. Note: Integration coverage is limited to supported engineering systems; custom integrations may require additional development.
Does Faros offer 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. For more details, visit the Faros Security & Trust Center. Note: API capabilities are subject to platform version and customer configuration.
Use Cases & Benefits
How does Faros help with real-time troubleshooting and developer support?
Faros enables organizations to connect AI token spend to shipped outcomes, validate model routes and agent context before deployment, and enforce policies that reduce risk and cost. For example, using AI tools like Devin for live troubleshooting, Faros's platform can attribute token usage to specific support outcomes, helping teams resolve issues in real-time and measure the impact of AI-assisted support. Note: Effectiveness depends on integration with engineering systems and team adoption.
What business impact can customers expect from using Faros?
Customers have achieved measurable results such as a 50% reduction in cost per task (using the Time Machine feature), improved engineering velocity, enhanced ROI visibility, and proactive risk mitigation. Case studies include Autodesk (improved team outcomes), Coursera (clear communication of engineering value), and SmartBear (scaling engineering with outcome measurement). Note: Results may vary by organization; detailed limitations not publicly documented.
Who can benefit from Faros?
Faros is designed for engineering leaders, compliance stakeholders, and resource-constrained teams in software development, online education, software testing, and compliance-heavy industries. It is especially valuable for organizations seeking to optimize AI engineering workflows, reduce costs, and ensure compliance. Note: Best fit for engineering-centric organizations; teams outside these domains may require alternative solutions.
Pain Points & Solutions
What problems does Faros solve for engineering organizations?
Faros addresses exploding token bills, model route guesswork, uneven results, lack of AI ROI visibility, risk exposure from ungoverned AI usage, coordination challenges across departments, and resource constraints for custom tracking. It provides token intelligence, outcome attribution, and governance to help organizations optimize spend and outcomes. Note: Some pain points may require organizational process changes for full resolution.
Can you share specific examples or case studies of Faros in action?
Yes. Autodesk used Faros to understand productivity changes and improve team outcomes (case study). Coursera leveraged Faros to communicate engineering value and track metrics (case study). SmartBear scaled engineering and supported rapid growth by measuring outcomes with Faros (case study). Note: Outcomes depend on implementation scope and team engagement.
Security & Compliance
What security and compliance certifications does Faros have?
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 may vary by deployment model; contact Faros for details.
How does Faros protect customer data?
Faros implements administrative, physical, and technical safeguards, including granular access control, secure deployment options (SaaS, hybrid, or on-premises), 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. For more, see the Faros Security & Trust Center. Note: Customers should review their own compliance requirements before deployment.
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: Implementation time may vary for complex environments or custom integrations.
What feedback have customers given about Faros's ease of use?
Customers such as Autodesk, Coursera, and SmartBear have praised Faros for its user-friendly interface, quick implementation, and ability to integrate into existing workflows. For example, Ben Cochran (Autodesk) highlighted actionable insights, Mustafa Furniturewala (Coursera) noted seamless communication of engineering value, and Vineeta Puranik (SmartBear) emphasized data accessibility for all organizational levels. See linked case studies for details. Note: User experience may vary by organization and use case.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, so customers only pay for what they use. Pricing scales with actual platform usage, allowing flexibility and value-driven ROI. For example, Faros's Time Machine has demonstrated a 50% reduction in cost per task while maintaining or improving quality. Note: For detailed pricing, contact Faros sales.
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 custom development.
How to Use Devin AI for Troubleshooting Developer Issues
Learn how to use Devin AI for troubleshooting complex developer issues. See how AI-powered debugging generates solutions and resolves problems instantly.
How to Use Devin AI for Troubleshooting Developer Issues
Learn how to use Devin AI for troubleshooting complex developer issues. See how AI-powered debugging generates solutions and resolves problems instantly.
Before AI coding agents, troubleshooting a customer issue often meant cutting calls short, reproducing steps later, and long back-and-forth cycles. Now, AI tools like Devin (the AI software engineer from Cognition Labs) can help engineers resolve problems live during support sessions, improving efficiency and customer satisfaction.
What can Devin AI do in a customer support scenario?
A big part of my job as a developer is helping customers debug tricky issues.
The usual flow looks like this:
The customer hits an error and shares it with us.
We go offline, dig into the code, try to reproduce the issue.
One or two days later, we send back a curl command to help troubleshoot or find a workaround.
But recently, I had Devin AI open during a live customer call to help with the troubleshooting.
The customer showed me the error, and instead of taking it offline, I asked Devin for help—live.
It read through the code, figured out how the introspection API worked, and generated a working curl command—in a different language, no less. All in seconds.
The customer ran the command while we were still on the call, and it worked. We could immediately determine the root cause on the customer’s infrastructure side and develop a clear plan to fix it, right then and there.
How does Devin AI help troubleshoot problems faster?
Devin combines two powerful capabilities:
Indexed codebase: It can quickly locate and understand relevant logic.
Cross-language translation: It can convert code (e.g., TypeScript) into other formats (e.g., curl commands) in real time.
Together, these features enabled us to troubleshoot the issue live with the customer, without delays or multiple follow-ups.
This experience highlights how AI can:
Reduce friction in customer troubleshooting.
Save engineers time by automating command construction.
Improve customer confidence through real-time problem solving.
In this example, we provided faster, smarter support—turning frustrating sessions into opportunities to impress customers.
Video walkthrough
Here’s a video walkthrough of how I used Devin AI for troubleshooting:
Full transcript: Using Devin to troubleshoot customer requests in real time
“One of the things that we sometimes have to work on as software engineers is customer support. And I want to show you one cool thing that I did with Devin that helped me a lot during a customer call.
We were trying to troubleshoot why a sync command was failing for a customer. And you know, in the pre-Devin world, we would have had to probably cut that meeting short and have a lot of back and forth while we figured out specific steps for them to reproduce the issue and try to isolate it.
So while we were in the meeting, I just went to Devin, and I just told it to give me a curl command to introspect the graph and to put placeholders for API URL key and graphs so that we could substitute it with the customers. The only hint that I gave it was that that functionality lives in the Faros JS client. So it's actually in this function here.
So imagine trying to construct a curl command based on this logic—you have to see what this does and then see what's in data and build the client schema and whatnot. That would take a good chunk of time. Certainly cannot be done live. But in a matter of seconds, Devin came up with an equivalent curl command that is equivalent to the logic that I was trying to test. We were able to test this live with the client without spending too much time and any back and forth.
I think this is one of the very powerful features that Devin has because it has all of your codebase indexed, but it's pretty amazing that it's also able to translate between languages. It's translating from TypeScript here to an actual crawl command.”
How to Use Devin AI for troubleshooting: Transform support from reactive to real-time
This approach didn't just save us hours—it fundamentally changed how we handle developer support challenges. What could have been a frustrating multi-day back-and-forth between teams became a productive, collaborative troubleshooting session that resolved issues in real-time.
I publish my thoughts on AI and experience with AI coding tools frequently. Follow me on LinkedIn for more tips on using AI coding agents.
Yandry Perez Clemente
Yandry Perez is a senior software engineer at Faros.
What is a work restart? Why AI is driving them up 66.7%
A work restart is a task sent back to development after review, QA, or deployment. Learn what restarts cost in developer time and AI tokens, and how to reduce them.
AI Industry
10
MIN READ
Comprehension debt: When AI speed outpaces human understanding
Explore the widening gap between what gets shipped and what devs understand. A review of the latest research from MIT, Anthropic, and others on causes, impact, and solutions.
AI Industry
12
MIN READ
What is a software factory? How it works
Learn how software factories use AI agents, orchestration, evals, and verification to automate engineering workflows and continuously improve software delivery.
.webp)
