DSH Task Board Brings Kanban-Style Agent Workflows to DeepSeek Harness
DeepSeek Harness users running multiple agent tasks lose visibility the moment a chat list grows beyond a handful of items. A kanban board with persistent state and explicit review gates turns ad-hoc prompting into a workflow that can survive restarts, making the tool usable for batch jobs and multi-step agent runs that need human judgment between stages.
DSH Task Board installs as a single plugin that surfaces a kanban board inside DeepSeek Harness Web. Tasks move through five states—Initialized, Running, Pending Review, Completed, and Failed—with manual approval gates and retry controls. It does not replace the existing session runtime; execution still runs through standard DSH Sessions, while the board handles task cards, status transitions, review workflows, and persistent storage.
Installation is a single npx command that adds one bundle, one package, and one Loader to the profile. The board supports both manual tasks for to-do tracking and Agent tasks that hand off to a DSH Session when model credentials are configured. Task cards carry a title, working directory, Agent Preset, requirements, acceptance criteria, and optional reference images.
Persistence is the headline feature: task records live in DSH Storage, not just React state. Refreshing the page, closing the browser, or restarting the Host restores the full board from an authoritative snapshot. The v0.1.0 release passed 168 tests across 20 test files with byte-identical builds and zero browser errors, though the public demo did not include live model calls.
Persisting task state in DSH Storage rather than React state is the architectural decision that makes the board more than a UI skin—it turns ephemeral chat sessions into durable workflow records.
The plugin's single-Loader design avoids the fragmentation common in plugin systems where Host, RPC, and UI components ship as separate entries that must be configured individually.
Explicit review gates and manual retry controls address a real gap in agent tooling: unattended agent loops that either succeed silently or fail without human recourse are not production workflows, they are experiments.