Start here – our FAQ covers the key things you need to know. Learn how to scale workflows, train AI copilots, and make your tools finally in sync.
Magic Coder is an agentic coding environment built directly into BridgeApp. It helps teams move from requirements to production-ready code by understanding tasks, discussions, documentation, architecture, and organizational standards in a single workspace.
For more information, please read documentation.
No. Magic Coder is designed to augment engineering teams, not replace them. Engineers continue making architectural decisions, evaluating tradeoffs, reviewing solutions, and defining priorities. Magic Coder helps automate implementation work, context gathering, and repetitive development tasks
Most AI coding tools work from a limited snapshot of context, such as a prompt, repository, or documentation export. Magic Coder operates inside BridgeApp, where tasks, discussions, decisions, documentation, and project knowledge already exist. This allows it to generate code with a deeper understanding of your team's intent, architecture, and standards.
Magic Coder can assist with:
Yes. Teams can establish development standards, coding conventions, review expectations, and project rules within BridgeApp. Magic Coder uses these standards when generating and modifying code.
An agentic coding environment is more than a code generator. Magic Coder can analyze requirements, plan implementation, understand existing architecture, write code, update repositories, create documentation, and support delivery workflows as part of a complete development process.
Yes. Magic Coder is designed to work with existing development workflows and integrates with GitHub and your current engineering stack.
Yes. Magic Coder analyzes your codebase and works within your existing architecture. It identifies reusable components, follows established patterns, and aims to extend your system rather than creating disconnected implementations.
Yes. Documentation is created as part of the development process rather than as a separate afterthought. Because Magic Coder has access to the task context, discussions, and implementation history, it can generate documentation that explains both what was built and why.
Because standards are defined centrally in BridgeApp, Magic Coder applies the same rules and conventions across tasks, repositories, and branches. This helps maintain consistency across teams and projects.
When assigned a task, Magic Coder can analyze related requirements, discussions, documentation, project knowledge, and historical decisions stored within BridgeApp. This allows it to generate solutions based on the full context of the project rather than a single prompt.
Yes. Magic Coder is designed for organizations that require centralized standards, governance, maintainability, and long-term visibility into development decisions.
Magic Coder is particularly valuable for:
Magic Coder addresses one of the biggest challenges in AI-assisted development: lost context. While code often survives, the reasoning behind decisions frequently disappears across chat threads, tickets, and meetings. Magic Coder keeps development work and project context connected, helping teams build software that remains understandable and maintainable over time.
Magic Coder runs inside BridgeApp. To get started:
Rather than optimizing solely for code volume, Magic Coder focuses on development quality and maintainability. Key indicators include reduced review comments, cleaner handoffs, better documentation, and less rework before deployment.
Yes. Magic Coder is currently available through a soft launch and can be accessed within the BridgeApp ecosystem.
More information on how to set up a Magic Coder can be found here.
Yes. A human can step in at exactly the points that matter — at review stages, approvals, and checkpoints between each stage.
Cost per completed task dropped roughly 10x — from hundreds of euros in human time down to tens of euros with AI.
A virtual team of AI agents that picks up a task, carries it through planning, execution, and review, and hands off a finished pull request — running the end-to-end development process for you. No human handoffs and copy-pasting between tools.
Work flows through defined roles, stages, review loops, and approval points. The process configures as kanban columns: To Do → planning → plan review → execution (writing code) → code review → re-check → merge to repo. Each agent gets a narrower job and the full context for that job, checks happen between stages instead of at the very end, and a human can step in at exactly the points that matter.
It gets caught at the next checkpoint, not three weeks later in production. There's a checkpoint between every stage. Different AI agents in the pipeline use documented steps at every point to validate results and keep working from a shared understanding.
An Architect agent, a CTO agent, a Backend agent, a Frontend agent, an Analyst, a QA agent. Any agent with any skills you need. Each one runs its own model under the hood — backend might run on Claude Code, frontend on Codex, whatever performs best for that job.
Yes. Because the pipeline is model-agnostic, anyone on the team — even a non-developer, say an AI engineer or marketing manager — can pick the model their agent runs on.
You have a full record at every step. When AI touches your codebase, you know what it did, why, and who signed off on it. The orchestration layer manages context and documentation at every step, so each AI agent down the line knows exactly who did what, and why, on the step before it.
Three pillars: Planning — before any code gets written, AI produces a plan and a human reviews, iterates, and approves it. Documentation — every step and every plan gets documented. Re-check cycles — they run multiple times before anything ships.
Inside project settings, you can find a Project Brief: connect your GitHub and GitLab repos, and our AI and Copilot index the entire codebase to understand the context. You add what you're actually building and which repo owns what. From there, you configure the process itself as kanban columns and define the roles for each lane.
A Service Account behaves similarly to a regular user within the platform. It can participate in projects, send messages, and perform actions based on its permissions.
The difference is that Service Accounts are intended for automated systems and integrations rather than human users.
Yes.
You can define an expiration date when creating or managing a Service Account. Once the expiration date is reached, the token will no longer work.
Open the Token tab and click Revoke.
The token will immediately become invalid and all API requests using that token will stop working.
Yes.
The Activity tab contains a history of actions performed by the Service Account, allowing administrators to review its usage and activity.
A Service Account provides a secure entry point for external systems through an API token.
An Agent is an internal automation component that performs tasks and workflows within the BridgeApp platform.
In short:
Yes.
You can control exactly what a Service Account can access and what actions it can perform through permissions and workspace access settings.
After deletion:
A token will be generated after the Service Account is created.
A Service Account is a technical user within a workspace. It has its own name, avatar, and authentication token, can perform actions, and appears in the system as a separate participant.
Only Workspace Administrators have access to create, configure, manage, and delete Service Accounts.
The Service Accounts section is only available to workspace admins.
Depending on its permissions, a Service Account can:
If you suspect a token has been exposed, immediately rotate or revoke it from the Token tab. This will prevent any further access using the compromised token.
Service Accounts follow the same permission model as other workspace members.
They can:
A Service Account can only perform actions explicitly allowed by its permissions.
Open the Service Account settings and navigate to the Token tab.
Click Rotate to generate a new token. The previous token will immediately become invalid.
Yes.
Service Accounts are specifically designed for secure integrations, automation workflows, API access, CI/CD pipelines, monitoring systems, and other production use cases.
Service Accounts are designed for integrations and automation.
For example, external systems such as CI/CD pipelines, deployment tools, CRMs, or internal applications can use a Service Account to send messages, create tasks, trigger workflows, or interact with BridgeApp through the API.
In BridgeApp, you can create an AI agent with ease using a low-code visual editor, even if you don't have any programming skills. To do this, open the visual Skill Editor in the BridgeApp (Agents → Flows → Tap on a skillset or choose the + New Flow button in the upper right corner). This will give you a graphical representation of the agent's logic and its ability to solve tasks.
You can swap the blocks around to change the sequence of actions (‘Creaty DB Entry’, 'Send message', "Call API") or describe the action in detail by specifying instructions, sources (e.g., databases,) and ways of processing them, as well as communication channels (e.g., chats or messengers).
More information on configuring AI agents in BridgeApp can be found here.
Sign up at BridgeApp. Go to Settings → Billing. The Compute Credits tab shows your balance and a daily spend graph. You can top up at any time. Tokens themselves are cheap: a million tokens cost about $1 – $4, depending on the model. But if your AI agents tackle very large tasks, the total bill can still add up.
You can find additional pricing details on our Pricing page.
There is a 'Model' field in the agent profile. Select and set the most appropriate one. The currently available options are: