Routa: AI Coding via Kanban Delivery Pipelines

The Shift from Chat to Delivery

Routa moves AI-assisted development away from chat-based interfaces toward a structured delivery pipeline. By treating AI coding as a series of tasks on a Kanban board, the tool enforces traceability, evidence collection, and quality gates. This structure prevents the common issue of context loss and messy, unmanaged chat threads, shifting the developer's role from managing AI prompts to overseeing a defined engineering workflow.

Workflow and Architecture

The tool functions as a coordination layer that organizes tasks into specific lanes: backlog, development, testing, and review. Instead of a single agent attempting to handle all aspects of a request, Routa allows for specialist agents to handle different stages of the delivery process.

• Task Management: Users define tasks within a workspace attached to a local repository, which the system tracks across a visual board rather than a linear conversation.
• Traceability and Evidence: Every action taken by an agent is logged, providing the user with traces and diffs that explain what changed and why.
• Protocol Support: The platform integrates with agent protocols like MCP (Model Context Protocol) and ACP to allow for modular agent interaction.
• Deployment: The tool is local-first and can be run via Docker using docker-compose up or through a dedicated desktop application, avoiding the need for mandatory cloud-based accounts.

Comparison to Existing Tools

Unlike chat-first assistants such as Cursor or Claude Code, which center on the conversation, Routa centers on the task lifecycle. While agent frameworks like CrewAI or LangGraph offer similar flexibility, they often require the developer to build the workflow infrastructure from scratch. Routa provides an opinionated, ready-made coordination layer that prioritizes visibility and process over raw conversational speed.