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 topic of this page—tracing code changes and releases—directly benefits from Token Engineering by connecting token spend to shipped code and business 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, providing a single source of truth for tracking spend, model/tool usage, and policy compliance. Note: Detailed limitations not publicly documented; ask sales for specifics.
How does Faros help answer questions like “Has this code shipped?”
Faros connects data from work management, source code, and deployment systems to trace code changes as they are merged, tested, built, deployed, and released. It unpacks what was bundled into each deployment artifact, showing exactly which tasks, commits, and features were shipped. Faros dashboards and automations provide real-time answers to questions such as “Did this change go out?”, “What did we release this week/month/quarter?”, and “If I deploy this service, what’s going to be shipped?” Note: Faros requires integration with your engineering systems for full traceability.
Features & Capabilities
What are the key features of Faros?
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.
Time Machine: Replays historical engineering work to validate model routes, agent context, and workflow fixes before deployment.
Policy Engine: Manages and enforces organizational policies, budgets, quotas, approved models, and routing rules.
Integration with 60+ engineering data sources: Connects to builder desktops, gateways, source control, ticketing, CI/CD, and incident management tools.
Dashboards and automations: Provide real-time visibility into code status, releases, and work in progress.
Note: Some advanced features may require additional configuration or integration effort.
Does Faros support integration with my existing engineering tools?
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 optimizes AI engineering workflows. Note: Integration coverage depends on the specific tools and systems in use; check the Faros Security & Trust Center for details.
Does Faros have an API?
Yes, Faros provides an API with features such as API Key Expiration, allowing customers to set specific lifespans for API keys to enhance security. The API enables integration with existing workflows and tools. Note: API usage may require configuration and adherence to security best practices.
Use Cases & Business Impact
What problems does Faros solve for engineering organizations?
Faros addresses challenges such as exploding token bills, model route guesswork, uneven results across teams, 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 while maintaining or improving quality. Note: Effectiveness depends on the quality of integrated data and organizational adoption.
What business impact can customers expect from using Faros?
Customers can expect measurable improvements in cost optimization (e.g., 50% reduction in cost per task), engineering efficiency, ROI visibility, risk mitigation, and strategic decision-making. Faros enables faster shipping of production code, enhanced compliance, and maximized outcomes per dollar. Note: Actual results may vary based on implementation scope and organizational context.
Who uses Faros? What industries and roles benefit most?
Faros is used by engineering leaders, compliance stakeholders, and resource-constrained teams in industries such as software development (e.g., Autodesk), online education (e.g., Coursera), and software testing/tools (e.g., SmartBear). It is especially valuable for organizations in compliance-heavy industries and those seeking to optimize AI engineering workflows. Note: Suitability depends on the organization's engineering complexity and compliance needs.
Can you share specific customer success stories with Faros?
Yes. Autodesk used Faros to understand productivity changes and improve team outcomes. Coursera leveraged Faros to communicate engineering vision and track key metrics. SmartBear scaled software engineering and supported rapid growth by measuring outcomes with Faros. These case studies are available on the Faros website. Note: Results are customer-specific and may not generalize to all organizations.
Implementation & Ease of Use
How long does it take to implement Faros? 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. Note: Full implementation timelines may vary based on integration complexity and organizational readiness.
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, and Vineeta Puranik (SmartBear) noted the platform's intuitive design for users at all levels. Note: User experience may vary depending on organizational processes and user training.
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: For detailed pricing, contact Faros sales.
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. Note: For the latest certification status, visit the 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 custom security policies (MFA enforcement, password history, idle session timeout, IP-based login restrictions). Faros complies with export laws and provides administrative, physical, and technical safeguards. Note: Security features may require configuration to align with organizational policies.
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 some custom development.
A product manager at a leading US bank had to drive to a branch to confirm a new ATM feature was live. Faros AI is delivering those answers to your Inbox or Slack.
A product manager at a leading US bank had to drive to a branch to confirm a new ATM feature was live. Faros AI is delivering those answers to your Inbox or Slack.
If you work in tech, you probably hear these questions more often than you’d like:
Did this change go out? Where is it now?
What did we release this past week/month/quarter?
If I deploy this service, what’s going to be shipped?
Whether you’re the person posing the question (customer success, product, marketing, business leader) or the one being asked (engineering manager, release manager), it’s frustrating nonetheless.
We should, by now, have an automated and self-serve way to get these questions answered definitely.
But for most organizations, that is not the case. Even if a ticket is done or a PR is complete, it’s often not quite clear whether the code shipped and value has been delivered.
Why Is Tracing Code Changes So Difficult?
Why did a product manager at a leading US bank have to drive to an ATM to see if their new ATM feature was finally live?!
Because it’s quite difficult to track the journey of new functionality through disconnected systems, especially as it’s changing shape along the way. Here’s why:
Functionality, whether a bug or feature, traditionally starts as a task in a work management system like Jira. This initial task phase describes what needs to be done and the why behind it.
From there, an engineer will translate it into code tracked through commits and PRs in version control systems like GitHub or GitLab.
Code is eventually merged and packaged into artifacts that are deployed using a deployment system, e.g., Circle CI or Jenkins. These deployed artifacts take the functionality through different environments like dev and QA before finally delivering it to customers in production. In large organizations or complex systems, the deployment pipeline may involve multiple stages, environments, and checks.
Tracing the code changes up and down this toolchain requires strong integration between the tools and an understanding of the relationships between the various artifacts that encapsulate them.
The larger teams become and the more distributed geographically and architecturally, the harder it becomes to just know. With a microservices architecture, different services might be deployed independently. This can make it challenging to know if a specific feature, which might span multiple services, is fully live.
Further complicating matters, organizations (and even groups within them) have different cadences for advancing code from dev to production, restricted by code cadences and policies.
And, while a powerful tool for controlled releases, the wide adoption of feature flags also introduces uncertainty. A feature might be deployed to production but turned off, leading to confusion about its live status.
Some companies try to solve this problem with better labeling throughout all stages, however, I’ve found this to be brittle and error-prone and it only adds to an already complicated process.
Is the only solution manually verifying the issue yourself? Driving to the ATM? Even if you could afford the hassle, often you simply can’t! You don’t always have access to the software, environment, or configuration in question.
The bottom line is that if you really need to know what’s going on and where functionality is, a fair amount of digging and inference is involved.
Eliminating the Wild Goose Chase
Faros AI has solved this problem for me, and it can for you too.
As a complete and extensible software engineering intelligence platform, Faros AI knits together data from work management, source code, and deployment systems to trace code changes as they get merged, tested, built and deployed, and ultimately released.
As I’ve explained above, this is hard stuff. When a deployment happens, the deployment system can tell you which artifact went out, or, at best, the most recent commit that was released. But what else was in that artifact? Normally, you wouldn’t know.
Faros AI has made it trivial to unpack what was bundled into an artifact so you can easily unwind everything that went out with a given deployment. Each code change is traced not just through its production release; it’s also connected to its corresponding product context through the associated task and its parent (epic, feature).
Faros AI unpacks a bundled artifact so you can easily unwind everything that went out with a given deployment
Here’s how I use Faros AI to utilize this information to answer those frequent “Has this code shipped” questions.
Did this change go out?
Below is a Faros AI chart that lets me and my colleagues easily see where we are on a current feature. I can see across the Jira ticket status, PR status, and which environment the change has made it to.
A Faros AI chart tracks a changes's Jira status, PR status, and current environment
In this example, my colleagues in customer success can see that the bug is still in development, waiting for a review. However, the second item — a feature — is already in our staging environment and just awaiting a production release.
With Faros, the team can get accurate information in seconds without having to ask PMs or engineers for updates on every item.
What did we release this past week/month/quarter?
Every organization has reporting cadences where it’s necessary to understand what was released in the past week, month, or quarter. This information is vital for updating documentation, notifying customers, and preparing marketing communications.
Personally, I also love to look at this information when I get back from vacation; it helps me catch up on everything I missed.
Here’s a dashboard on Faros that summarizes what’s been released over the last 30 days. Looking at the Released Tasks with Epic and Sha table, I can see:
The ‘Mock data feed takes ‘now’ time as input’ task is done and all related commits have been released
The ‘Update CLI’ task is being worked on incrementally; some work has been released but the overall task is still in progress.
A Faros AI dashboard summarizes what's been released over the last 30 days
Beyond a dashboard view, I utilize Faros automations to send a weekly update to our team on Slack and an email summary to leadership.
A Faros AI Slack notification sends a weekly update of what's been shipped to production this week
If I deploy this service, what’s going to be shipped?
With different teams contributing to the same code base, it’s important to know what I’ll be releasing when I pull the trigger.
This comes up often for us at Faros AI for services involving contractors or team members in different time zones.
Not everyone can be in the go/no-go decision about a release. Having a Faros AI dashboard to check what will go out gives me the peace of mind I need to kick off a release and the confidence to know what is about to go live.
This dashboard of “Stuff in Dev” has all the work that will go out in the next production release.
A Faros AI list of all the changes that will go out in the next production release
Have we closed out completed work?
Data hygiene can be a struggle, more so when the work on an epic or feature is distributed across multiple teams or contributors — each completing their work at a different pace. The unitary stories, tasks, or sub-tasks move to ‘Done’, but often the parent is forgotten in some “in progress” state.
At large organizations, it does become hard to know which epics should be closed out and when.
With Faros AI automations, you can create alerts to notify the epic owner when all the children stories are complete and the epic itself is still ‘In Progress’. This way, they can be sure to tie up any remaining activities required to close the parent.
A Slack notification from Faros AI notifying the epic owner when all child stories and tasks are complete
Visibility Is a Productivity Game Changer
Our current economy has everyone trying to do more with fewer resources. GitHub Copilot is unlocking developer productivity. Software engineering intelligence platforms are doing the same for managers and leaders.
If you want visibility similar to what I have into code changes, deployments, and releases, you might want to try Faros AI. Our mission is to maximize the effectiveness and efficiency of software engineering, and that includes eliminating the scavenger hunt part of our jobs.
Natalie Casey
Natalie is a software engineer, and most recently—a forward-deployed 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)
