Projects serve as containers that organize your AI agent runs around a specific task, feature, or initiative. By grouping related runs together, you can:

  • Maintain context continuity across multiple agent interactions
  • Share common documentation and requirements with all agents
  • Track progress on complex, multi-step tasks
  • Collaborate effectively with team members on larger initiatives (coming soon)

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Creating Projects

There are two primary ways to create a project:

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1. Create from Projects Page

Navigate to the projects page and click “New Project” to create a project from scratch.

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2. Create from Design Document

Generate a project directly from an existing design document or specification.

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Why Use Projects?

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Context Window Limitations

All AI agents have limited context windows, meaning they can only process and remember a finite amount of information before their performance degrades. This limitation becomes critical when working on:

  • Large codebases with complex architectures
  • Multi-step feature implementations
  • Tasks requiring deep domain knowledge
  • Long-running development initiatives

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The Solution: Shared Context

Projects solve this by providing a shared context document that all agents can reference. This approach:

  • Eliminates context loss between agent interactions
  • Ensures consistency across different implementation phases
  • Reduces repetitive explanations of project requirements
  • Improves agent performance by providing relevant background

Here’s a proven workflow for maximizing project effectiveness:

  1. Deploy a research agent to analyze your codebase and understand the current architecture. Generate a comprehensive design document that outlines requirements, constraints, and implementation strategy
  2. Create a project from your design document with clear naming.
  3. Trigger specialized agents for specific implementation tasks. Leverage the shared context to maintain consistency across agents

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Benefits

By using projects effectively, you’ll:

  • Save time by eliminating manual context copying between chats
  • Organize your work with clear project boundaries and run history
  • Improve performance through consistent, well-informed agent interactions
  • Scale your development process for larger, more complex tasks
  • Maintain quality through shared understanding and documentation