AI agents are moving quickly from simple chat assistants to tools that can work independently on software tasks. They can inspect code, propose changes, run tests, and gradually complete assignments. Once you start using more than one agent at a time, a new question appears: who coordinates them?
Orca is built for that coordination layer. It does not focus on the model itself or on a single conversation. It focuses on operating agents as a system: tasks, priorities, parallel work, safety boundaries, and visibility into what is happening right now.
Why one agent is not enough
A single agent in a terminal is useful for smaller tasks. Larger work needs more than an open session. You need to know which task is ready, what depends on what, how many agents may run in parallel, and when the work must stop because the risk is too high.
A typical example:
- one agent prepares a UI change,
- another verifies tests,
- a third handles a follow-up task,
- a sensitive part of the project waits for manual approval.
Without orchestration, this quickly becomes a set of disconnected terminals. Orca adds a control layer above them.
Missions instead of isolated sessions
The core concept is a mission. A mission groups related tasks under one goal, defines the maximum number of concurrent agents, and sets the autonomy level available to them.
This means you do not have to start every agent manually and watch every session separately. Orca continuously evaluates which tasks are ready, respects dependencies, and deploys agents according to the rules of the mission.
The result is controlled parallel work — not several agents launched side by side without structure.
Real-time work visibility
Orca monitors agent progress and turns it into states that are useful for project control. You can see whether an agent is working, waiting for input, finished, or blocked.
This matters most when an agent runs longer than a few minutes. For longer tasks, you do not want to guess whether it is still progressing or waiting on a question nobody noticed. You need a clear signal and the ability to intervene.
API, CLI, and web interface
Orca is designed to be used in several ways. It provides an API for integration, a CLI for fast terminal workflows, and a web interface for monitoring tasks, missions, and active agents.
The same system can therefore support different modes of work:
- quick task creation from the terminal,
- supervision of running agents in the web interface,
- integration with other automation through the API,
- structured execution of larger changes through missions.
Safety as part of the design
Agent autonomy only makes sense when clear boundaries exist. Orca therefore includes safety rules that can stop risky tasks before an agent starts working.
Sensitive areas such as authentication, database changes, payment logic, or destructive operations can require manual approval. This gives you the speed of agents without blindly allowing every action to proceed.
Where Orca is heading
Orca is aimed at practical AI development operations: multiple agents, clear assignments, continuous supervision, safety boundaries, and the ability to scale work without losing control.
It is not just another interface for chatting with a model. It is an operational layer for the moment when AI agents become a regular part of the development workflow and need to be managed as carefully as any other team process.
For projects where AI is used daily, that difference matters: less manual switching, better visibility, and controlled autonomy instead of blind trust.