Development with your own agents
Streaming chat, tool execution, visual diffs, and subagents working in plain sight.
orbit mindcode
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IDE · AI agents
Your agents. Your context. Your infrastructure.
orbit mindcode is the desktop IDE where your own AI agents code with your company's real context: Jira and Linear tasks, Confluence, Drive, and OneDrive documentation, and engineering rules turned into hooks that block what must not happen. The agent engine runs locally, per project.
orbit mindcode
ide · ai agents
01 · Context
The problem we are solving.
Teams already use AI assistants, but they use them blind: the assistant knows neither the sprint tasks nor the product's functional documentation, and it does not obey the company's engineering rules. Any CTO knows the result: code that ignores the agreed architecture, specifications that live in Confluence and never reach the editor, and developers copying context by hand across five tools. orbit mindcode brings the company's context and rules inside the IDE.
02 · What it is
orbit mindcode in one sentence
A complete desktop IDE, with editor, terminal, git, and file explorer, embedding our own agent-orchestration engine: every project starts its local instance with its own database, and on top of it sit a chat that shows every tool the agent runs, a studio to build and govern your agents, and a panel that brings in your company's tasks and documentation.
03 · How it works
Four steps. No magic.
From the open project to the agent executing, with everything in plain sight.
01
You open your project
The IDE detects the workspace, starts the agent engine locally, and creates the project's database inside the folder.
02
You configure your agents
From scratch or from 12 templates: name, leader, worker, or hybrid role, one model per each of the 3 tiers out of 21 available, allowed tools, your own prompts, and hooks that block actions before they run.
03
You connect your sources
Jira, Linear, Confluence, Google Drive, and OneDrive sync in the Builder panel, and tasks and documents are browsed without leaving the IDE.
04
You chat and the agent executes
You watch every token live, every tool executed with its diffs and terminal output, and the activity of delegated subagents. Cancel whenever you want.
04 · What it does
Where it goes to work.
The IDE our own engineering team works with.
Per-team AI governance
The tech lead defines agents with restricted tools and blocking hooks. The TDD template makes it impossible to write code before the test exists.
The sprint inside the editor
Jira and Linear issues listed and filtered by status and assignee, in the same panel where you code.
Documentation that updates itself
The agent reads documentation from Confluence, Drive, and OneDrive, builds from it, and keeps the specification alive as the product changes.
Module and use-case specs
An agent maintains the module context and use cases in Markdown inside the repository itself.
Voice dictation in Spanish
The recording is transcribed and lands in the chat without typing a line.
05 · Benefits
What changes when you have it.
Five concrete differences from the AI assistant your team already uses.
Request demo1
Your own agents, not a black box
Role, model per tier, allowed tools, prompts, and instructions are defined by the client, and agents export and import as JSON.
2
Orchestration on infrastructure you control
The agent engine runs as a local binary on the developer's machine, and sessions, messages, and agents live inside the project itself.
3
Executable governance, not aspirational
8 hook events, one of them able to block the action before it happens: the difference between asking the AI to follow TDD and making it impossible not to.
4
Company context reaches the editor
Jira and Linear tasks and Confluence, Drive, and OneDrive documents in the same panel where you code, with documentation as the starting point instead of a debt.
5
A complete IDE, not a plugin
Editor, a terminal the agent can use, git, file explorer, and 7 bundled extensions. It does not depend on the editor you already use or anyone's extension policy.
06 · Video
Let us see it in action.
The agent explores the codebase with real tools, the fleet of agents with its model and role in plain sight, and the company's tasks and documentation synced from the same panel.
07 · Industries
For teams that no longer improvise with AI.
01 / Software and consulting
Teams standardizing how their people use AI
Agents and rules shared per project.
02 / Financial services
Control over where orchestration runs
The control plane is local and the destination of model calls is configurable.
03 / Companies with a formal PMO
Jira and Confluence as the source of truth
Development starts from that documentation and keeps it alive.
08 · Before and after
What changes, side by side.
An honest comparison between today's AI assistant and an IDE of your own agents.
Before / Without orbit mindcode
Today's day-to-day
- ✕ The assistant knows neither the sprint tasks nor the functional documentation
- ✕ A single behavior, decided by the tool's vendor
- ✕ Engineering rules are asked for in writing and broken anyway
- ✕ Orchestration runs in the editor vendor's cloud
- ✕ Context copy-pasted by hand across five tools
After / With orbit mindcode
The day-to-day with your own agents
- ✓ Company tasks and documents inside the same IDE
- ✓ A fleet of agents with role, model, and tools you define
- ✓ Hooks that block the action before it happens
- ✓ A local engine per project, with its own database
- ✓ The agent reads the context and maintains the repository's specification
09 · Metrics
What ships with it.
Figures verified in the product's code, not commercial estimates.
12
Agent templates across 5 categories
21
Selectable models, across 3 cost tiers
5
Company systems connected to the IDE
8
Hook events, with pre-execution blocking
10 · Impact · No brainer
Your team already uses AI. The question is who governs it.
With a commercial assistant the vendor decides the behavior. Here the behavior is your company's asset: defined, exported, and shared across projects.
Run the discovery with rocky12
Tools assignable per agent
3
Roles with hierarchical delegation
1
Local database per project
7
Bundled VS Code extensions
11 · Security and governance
The control plane is yours.
We claim no certifications. We claim an architecture where orchestration is local and you choose where model calls go.
Local
Engine and data per project
The agent engine runs as a local binary and sessions, messages, and agents live in a database inside the workspace itself.
Models
Configurable routing
Model calls go out to the provider the client authorizes, configured in a single file.
Hooks
Blocking before execution
8 governance events, one of them able to stop the agent's action before it touches anything.
Access
Read-only and system keychain
Task and documentation integrations connect with read-only scopes and tokens are stored in the operating system keychain.
12 · Contact
Let us get orbit mindcode building with you.
30-minute demo with your real repo and workflow. If it fits, we define a measurable pilot. If not, we tell you which orbit product does fit.