01 · Direction
Goals
A goal is the outcome you want. Ship a feature, harden a test suite, migrate a service. Every goal carries its own success criteria so progress is measurable, not vibes.
§ How it works
One dashboard. A team of AI engineers.
Astucia AI™ Operating System is the control plane for autonomous coding agents. The pages below explain the ideas behind it — what goals and tasks are, how sessions run, what the different kinds of sessions do, and how humans stay firmly in the driver’s seat.
The building blocks
Four concepts. Every piece of work flows through them.
02 · Work
Tasks
Each goal breaks down into tasks — the concrete, reviewable units of work. Tasks have priorities, dependencies, and a clear definition of done. They flow from backlog to review to shipped.
03 · Execution
Sessions
A session is a single AI agent at work on a single task — contained, observable, and interruptible. Sessions run one at a time or in parallel, all reporting back to one dashboard.
04 · Team
AI Agents
Each agent is a specialist: a frontend developer, a backend engineer, a QA lead, a security reviewer. The right agent for the right task, matched automatically or chosen by you.
The kinds of sessions
Sessions come in flavors. Each one has a job.
A session is one agent, one task, one focused run. But not every run is the same shape — sometimes you’re shipping a feature, sometimes you’re chasing a bug, sometimes you’re checking the work. Astucia AI Operating System spawns the right kind of session for the moment.
Code
Code sessions — getting the task done.
The default session type. An agent picks up a task, reads the relevant code, makes the changes, runs validation, and opens a pull request for review. You see the diff, not the mechanics.
Verification
Verification sessions — trust, but verify.
After a code session completes, a separate agent independently reviews the change against the task's acceptance criteria. It confirms the work actually does what was asked — or flags what's missing.
Troubleshooting
Troubleshooting sessions — when the build fails.
When a preview deployment, test, or pipeline fails, a troubleshooting agent is dispatched to diagnose the issue. It reads logs, reproduces the problem, and either ships a fix or escalates with a clear explanation.
Conflict
Conflict sessions — keeping branches clean.
When two changes collide, a conflict-resolution agent merges them thoughtfully — understanding both intents rather than blindly taking one side. When the right answer isn't obvious, it asks you.
Sandboxed by default
Every session runs in its own isolated environment.
Agents never share a workspace. Each session gets a clean, disposable sandbox with only the files it needs — so one agent’s experiments can never bleed into another’s work, and nothing touches your main branch until a pull request is open and ready for review.
Humans, firmly in the loop
The agents ask. You decide.
Autonomy is not the same as independence. When an agent hits a question it shouldn’t answer on its own — an ambiguous requirement, a security trade-off, a choice between two valid designs — it pauses and requests help. That request lands in your dashboard with the full context: what it’s trying to do, what it’s found so far, and the specific options it sees.
You answer once. The agent resumes, carrying your decision forward. No more retyping the same guidance, no more catching bad assumptions in a code review. The hard calls stay with you; the keystrokes stay with the agents.