Coordinate human and AI work around shared tasks

What is Dart?

Dart's current platform centers on people and agents working from a shared plan. Teams can create custom agents or connect existing tools, assign work, and keep results tied to tasks and goals. The official examples span coding, research, design, customer support, and operations. Its purpose is to make delegated work visible in the same place as the team's decisions, instead of leaving context scattered across separate conversations.

Give the task enough context to be reviewed

A useful agent assignment needs a concrete outcome, relevant source material, constraints, and a definition of completion. Dart describes shared tasks and context for connected agents, including development work with repository information. Separate an open research question from an implementation request so the responsible person can judge the result appropriately. Clear task ownership remains useful even when several agents contribute along the way.

Keep handoffs connected to the plan

The platform describes a lead agent coordinating specialist work and bringing people in when their judgment is needed. Results, review comments, and technical decisions can remain alongside the task. Define which handoffs require a human decision, especially when work moves from a draft into a customer-facing action. That makes progress easier to assess than a sequence of disconnected messages announcing activity without a reviewable output.

Features to explore

• Shared human and agent tasks
• Custom and connected agents
• Goals and working context
• Agent coordination and handoffs
• Task-linked results and reviews

Who is Dart for?

Dart fits teams already using AI tools for substantial work and needing a shared coordination layer. It is relevant when assignments involve several contributors and decisions, sources, and final results should remain traceable to the same task.

Before you get started

Review connected-agent permissions and define approval boundaries for external actions. Verify results against task requirements; delegation and visible progress do not guarantee correct implementation, complete research, or appropriate access to confidential information.