AutonomyAI

What is AutonomyAI?

AutonomyAI is a production-grade AI development platform designed for product teams, with the core execution engine named Fei Studio. It connects existing code libraries, components, design systems, and engineering standards, enabling the transformation of product ideas into visual designs, production-ready code, specification documents, and merge requests ready for review by engineers.

The goal of this product is not to allow AI to go live directly, bypassing the engineering team, but rather to enable product managers and designers to carry out more implementation tasks within the actual system, with engineers then reviewing and integrating those changes. It resembles a cross-functional product delivery framework, rather than a code completion tool for individual use.

Core functions

• Code library understanding: scanning components, styles, interfaces, events, dependencies, and architecture.
• Multiple task inputs: acceptance of product requirements, tickets, screenshots, design drafts, and textual descriptions.
• Structured planning: Break down the modifications and implementation steps based on the existing infrastructure.
• Visual prototype: Provides a viewable and comparable version before the final code is generated.
• Production-grade code: Reuses real components and adheres to the organization’s coding standards.
• Specification document: Records requirements, decisions, implementation details, and the complete change history.
• Merge request: Create clean code changes that can be reviewed by engineers.
• Knowledge Center: Continuously updates the project context as the codebase changes.
• Multi-agent execution: A single task can coordinate dozens of internal execution steps.
• MCP integration: Brings the capabilities of Fei Studio into compatible AI development tools.

How does Fei Studio work?

1. Connect to the enterprise’s Git repository and grant the necessary read and write permissions.
2. The platform scans the code structure, design systems, interfaces, styles, and engineering conventions.
3. Product managers or designers submit requirements, screenshots, tickets, or design files.
4. Fei broke down the ideas into plans and alternative options that are in line with the existing architecture.
5. The system generates visual previews, production code, and accompanying specification documents.
6. After verification is completed, create a separate branch and a merge request for review.
7. Engineers decide whether to merge the code after checking it for issues, conducting tests, assessing security aspects, and considering the impact on the architecture.

Code library understanding

Fei Studio connects to Git repositories and creates a model of the code base; according to its official website, this allows one to understand a project in a short amount of time. It takes into account not only the content of the files but also CSS specifications, interface patterns, data connections, authentication mechanisms, events, third-party libraries, and rendering settings.

• Project directory, module boundaries, and main technology stack.
• Existing components, design tokens, styles, and responsive rules.
• Interfaces, data retrieval, caching, and state management methods.
• Locations for authentication, single sign-on, and permission checks.
• Events, tracking points, forms, and business processes.
• Requirements for testing, formatting, building, and continuous integration.
• Establish common code patterns and review standards for the organization.

Task input method

Output for three tasks

Each completed task yields three main types of outcomes: a visual prototype, production code that complies with the organization’s standards, and a merge request that includes detailed specifications. This allows the engineering team to examine the user experience, the implementation details, and the underlying requirements at the same time.

How to submit a high-quality task

1. First, state the user’s problem and the business outcomes that wish to be improved.
2. Specify the affected pages, roles, processes, and areas that cannot be modified.
3. Add acceptance criteria, boundary conditions, error states, and permission requirements.
4. Provide relevant designs, screenshots, tickets, and the locations of existing components.
5. Fei is required to first submit a plan along with several alternatives; direct modifications to high-risk code are not allowed.
6. After selecting a solution, the implementation is generated, and the visual prototype along with the specifications are viewed.
7. Run project tests, security checks, and formatting tools before creating a merge request.

How product managers use it

• Transform product requirements from textual specifications into actionable changes in the actual product.
• Quickly compare multiple implementation options, rather than waiting until the project schedule is set to conduct evaluations.
• Improvements to the interface and support for feedback, with clearly defined scope of handling.
• Create demonstrations based on real code for stakeholders.
• Complete the acceptance requirements and change explanations before submitting the project for review.
• Collaborate with the engineering team using merge requests rather than isolated prototypes.

How designers use it

• Explore user experience solutions on real design systems and existing components.
• Convert Figma files or screenshots into functional interface variants.
• Check different screen sizes, states, and interactions, rather than just looking at the static canvas.
• Restructure the legacy interface while maintaining consistency in components and styles.
• Place design decisions, implementation details, and code changes in the same task.
• Verify visual quality and product processes prior to engineering review.

How engineers use it

• Assign the product and design teams clear front-end implementation tasks.
• Reduce the repetitive task of retranslating prototypes into production code.
• Receive merged requests that include specifications, visual previews, and the complete context.
• Use existing tests, static analysis, and code reviews to control quality.
• Retain ultimate authority over architecture, security, data, and deployment decisions.
• Focus time on platforms, complex backends, and high-risk technical issues.

Common use cases

• Validate product ideas: Generate multiple runnable versions in actual code.
• Improving existing functions: Make changes directly once issues are identified through feedback or metrics.
• Supports changing feedback: Allows for moving reproducible customer issues into merge requests.
• Stakeholder demonstration: Presenting the solution using a real product environment.
• Accelerate feature delivery: Handle front-end and product tasks in the backlog queue.
• Enterprise customization: Delivering a customer-specific experience on shared products.
• Legacy interface refactoring: Reuse modern components to replace old implementations.
• Design system alignment: Fix inconsistent styling and component usage.

MCP server

AutonomyAI provides MCP servers that enable the production and delivery layer of Fei Studio to be connected to Claude Code, Cursor, or other compatible clients. This allows teams to remain within their existing AI development environment in order to utilize code libraries for understanding, task execution, and delivery.

• Fei Studio can be invoked within a unified AI client, eliminating the need to switch between different tools frequently.
• Reuse AutonomyAI’s understanding of real codebases and design systems.
• Combine task planning, implementation, verification, and request merging into a single process.
• Configure restricted warehouse and action permissions for different users and agents.
• Retain records of tool calls, code changes, and manual approvals.

Agent Knowledge Hub

The knowledge center is updated as the code repository evolves, thereby providing the necessary context for the projects and reducing the need to re-explain the architecture for each task. Near-real-time updates still require verification; the team should ensure that the branches, the generated code, and the current production version are consistent with one another.

• Continuously synchronize changes in components, interfaces, and project architecture.
• Document the organizational coding standards and the way the system is designed to be used.
• Reuse the confirmed context for subsequent tasks.
• Reduce the reuse of old components, obsolete interfaces, and faulty patterns.
• Re-checking is triggered in the event of a large migration or branch differences.

Multi-agent task execution

A single user task can trigger more than 40 internal agent steps, and the platform charges based on the completion of the task as a whole, rather than on the individual steps or user accounts involved. This makes billing easier to understand for users; however, it is still necessary to refer to the current rules to determine whether a complex task counts as one task and whether attempts to restart a failed task consume additional credits.

Free Forever – the free version

The free version is suitable for individuals or small teams to explore enterprise-grade AI-assisted development; it includes 1 user, 1 code repository, up to 200 components, UI Tweak workflows, and community support. It does not provide the annual task quotas nor the full team capabilities available in the paid versions.

Prices of paid packages

Seed, Starter, and Teams all display monthly prices, but billing is done on an annual basis. Scale is intended for medium-sized, large organizations, or regulated entities, and it offers customized pricing based on tasks, service levels, security, and compliance requirements.

Comparison of package features

Eligibility for the Seed package

The Seed program is available only to startups that have already received seed funding and have fewer than 15 employees. Before making a purchase, it is necessary to verify eligibility, provide proof of funding, determine how the number of employees is counted, and find out the cost of migration once the future requirements are met.

How to choose a package

• The workflow for the personal verification interface can be fine-tuned; Free Forever can be used first.
• Early-stage startups that meet the financing and headcount requirements can be evaluated for Seed funding.
• The Starter plan is suitable for small teams that need integration with Jira, Linear, Figma, Slack, and Storybook.
• Cross-functional teams that need more tasks, Designer Suite, and model keys of their own can consider Teams.
• Companies that require a dedicated service level, data residency, or a customized component library should inquire about Scale.
• Estimate costs based on the expected number of tasks completed rather than the number of employees, as the pricing plans do not impose any limits on the number of users.

How to calculate the actual cost of a task

The figures above are merely simple estimates obtained by dividing the public subscription price by the annual task quota; they do not take into account costs related to internal audits, testing, model keys, implementation, or unfinished tasks. The actual return on investment should be determined by comparing the time saved in completing each type of task with the costs associated with subsequent modifications.

Safety and compliance

• Industry-standard encryption is used for transmitted and static data.
• Internal access is restricted through single sign-on, VPN, and two-factor authentication.
• Continuously record logs of authentication processes, management operations, and interface usage.
• The official statement is that customer data is not used to train AI models.
• By default, customer data is stored in the AWS environment in the United States.
• Corporate clients can discuss data residency solutions that meet the requirements of their jurisdiction.
• It supports the integration of built-in model keys and customer-managed models.
• The development process includes code review, static analysis, and dependency risk checking.

Authentication and compliance status

The official compliance page states that AutonomyAI has achieved SOC 2 compliance as of June 2025, and notes that its infrastructure adheres to the principles of ISO 27001. The page also indicates that efforts are still underway to achieve full GDPR compliance; therefore, the fact that certain principles are followed should not be interpreted as meaning that all certifications have been obtained.

Code review and release governance

1. Risk levels are determined based on the change catalog, data access, and business impact.
2. Low-risk documents and test changes can follow a faster review process.
3. Ordinary product codes must pass testing, static analysis, and engineer review.
4. Identity, billing, personal data, infrastructure, and migration require additional reviewers.
5. Use feature toggles, preview environments, and phased releases to minimize the impact.
6. After merging, monitor error, performance, and user metrics, while retaining rollback options.
7. Regularly audit the merge requests created by AI, as well as the approvers and online results.

Which teams are suitable?

• SaaS product teams that possess mature codebases and design systems.
• There is a significant backlog of product requirements, but engineering resources need to be focused on addressing complex issues within the organization.
• The goal is to have product managers and designers submit proposals for production changes that can be reviewed.
• The design team needs to quickly test the experience scheme in actual code.
• Companies that require AI code to go through existing merge requests, testing, and engineering reviews.
• There is a need for cross-functional teams that can be formed without any limits on the number of users, and whose size is determined by the volume of work to be carried out.

Situations that are not very suitable

• Individuals who have no existing code library and want to create a simple demonstration website from scratch.
• Teams that do not use Git or a pull request review process.
• Early projects in the codebase lack tests, design systems, and clear engineering standards.
• It is hoped that AI can modify the structure of production systems directly, without going through any engineering reviews.
• Tasks are primarily focused on teams dealing with underlying infrastructure, complex algorithms, or large-scale backend migrations.
• Projects with a high level of sensitivity cannot allow code and metadata to be transferred to third-party hosting environments.

Product advantages

• Work directly on the actual code repository and design system, reducing the need to redo prototypes.
• It simultaneously outputs the visual design, production code, specifications, and merge request.
• Provides a common delivery object for products, design, and engineering.
• The knowledge center is updated alongside the code, reducing the need to recreate project contexts repeatedly.
• The paid plan has no limit on the number of users and is suitable for cross-functional collaboration.
• It offers enterprise security, logging, BYOK, and data residency options.
• The MCP server can be connected to existing AI development environments.

Usage restrictions

• The generated code may still have issues related to logic, security, performance, and maintainability.
• The performance of a platform depends heavily on the quality of the existing code, the level of test coverage, and the specifications for its components.
• The pricing plans are billed on an annual basis, with the Starter and Teams plans being more expensive.
• Rules for task counting, retry on failure, and splitting complex tasks need to be confirmed before purchase.
• The default data is located in the United States; organizations that are sensitive to where their data is stored need an enterprise solution.
• Full GDPR compliance is still being pursued, and the purchaser is required to carry out the legal reviews on their own.
• Explorer Mode and the connection to certain business data still fall under the category of planned capabilities.
• The fact that product managers and designers can write code does not mean they can bypass engineering ownership.

Privacy and data considerations

• Only connect the warehouses, branches, and components required to complete the pilot.
• Delete test keys, customer data, and samples that should not be sent to the model.
• Confirm the scope of processing for suppliers, sub-processors, logs, and vectorized metadata.
• Configure permissions separately for OpenAI, Anthropic, or custom model keys.
• Establish a process for deleting data when employees leave, projects are completed, or suppliers are changed.
• The regulatory team should specify in the contract the rights regarding presence, backups, incident notification, and auditing.

GitHub and the open-source status

The AutonomyAI core platform and Fei Studio are not open-source projects. As of the time of verification, no official GitHub repository associated with these commercial products was found; similar projects such as Autonomous, Autonomi, and Autonoma that appeared in search results are not the same product.

Basic information

Recommendation score

The comprehensive recommendation score is 4.5 out of 5 points. AutonomyAI is suitable for product teams that already possess mature code libraries, design systems, and code review processes; it helps to reduce the need for repeated handovers from the product stage to the engineering stage. However, costs, the number of tasks involved, and data compliance issues need to be carefully evaluated.

Frequently Asked Questions

What does AutonomyAI do mainly?

It converts product requirements, screenshots, or designs into visual prototypes, production code, specification documents, and merge requests.

Will Fei Studio release the code directly?

It creates a merge request that needs to be reviewed; engineers still need to check it and decide whether to merge it and release it.

Do product managers need to know how to program?

It is not necessary to write code oneself, but one must be able to describe requirements, evaluate the results of the product, and review them together with engineers.

Is there a free version?

Yes, the free version includes 1 user, 1 repository, up to 200 components, and UI Tweak workflows.

How much is the Seed package?

The settlement is done on an annual basis, amounting to 125 dollars per month; it includes 300 tasks per year, but there are restrictions regarding financing and the number of people involved.

How much is the starter?

The settlement is done on an annual basis, which amounts to $425 per month; this includes 1,200 tasks per year as well as five team integration features.

How much is Teams?

The billing is done on an annual basis, amounting to 1,000 dollars per month; this includes 4,000 tasks per year, as well as BYOK and Designer Suite.

Will the customer code be used to train the model?

Although officials state that customer data will not be used to train AI models, companies should still verify the logs and the list of sub-processors involved.

Is MCP supported?

Yes, the capabilities provided by Fei Studio can be utilized in compatible AI development clients.

Is AutonomyAI an open-source tool?

No, both the core platform and Fei Studio are commercial hosting services.