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PlanBan (Clabby): A Local Command Center for Agent-Assisted Software Work

PlanBan (also known as Clabby) is a localized command center for agent-assisted software work, providing a dashboard view synchronized with issue trackers and a deterministic workflow engine. It supports multi-concurrent workflow management, git worktree integration, and state transitions with an override mechanism to ensure critical steps are not skipped.

智能体工作流引擎问题跟踪器git工作树RustCLI工具软件工程项目管理确定性执行人机协作
Published 2026-06-14 03:46Recent activity 2026-06-14 03:50Estimated read 9 min
PlanBan (Clabby): A Local Command Center for Agent-Assisted Software Work
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Section 01

PlanBan (Clabby): Guide to the Local Command Center for Agent-Assisted Software Work

PlanBan (also known as Clabby) is a localized command center for agent-assisted software work, designed to solve coordination dilemmas in AI agent-assisted development. It provides a dashboard view synchronized with issue trackers and a deterministic workflow engine, supporting multi-concurrent workflow management, git worktree integration, and state transitions with an override mechanism to ensure critical steps are not skipped. The project is developed and maintained by TimmyMC, with source code hosted on GitHub (link: https://github.com/TimmyMC/PlanBan), released on June 13, 2026. Its core value lies in balancing agent autonomy and human control, offering teams a reliable and flexible AI-assisted development coordination solution through the "human-in-the-loop" design philosophy.

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Section 02

Problem Background: Coordination Dilemmas in Agent-Assisted Development

With the widespread application of AI agents in software development, developers face coordination challenges: How to maintain control over workflows while leveraging agent capabilities? When multiple agents run concurrently, multiple terminal sessions are active, multiple git worktrees are under parallel development, and multiple issue tickets are in transit, how to maintain a clear overall view?

Traditional solutions tend to two extremes: either fully rely on agent autonomy (risking loss of control) or fully rely on manual coordination (costing low efficiency). PlanBan is positioned as a workflow execution framework—enforcing user-defined workflows, with users responsible for thinking and decision-making, trying to find a balance between the two.

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Section 03

Core Architecture and Functional Features

Core Architecture

PlanBan adopts a layered design: the core engine (clabby-core) is completely UI-independent, the CLI is a lightweight driver, and a graphical panel based on Tauri and React will be launched in the future. The architectural principle emphasizes that external dependencies (issue trackers, version control, agents) are abstracted through command templates, with no hardcoding—configuration changes can adapt to new workflows.

Functional Features

  1. Tracker Synchronization: Pulls issue tickets via configurable commands (e.g., acli/jira/gh), with the tracker as the source of truth; external changes are marked as "diverged", and users push changes to coordinate merging.
  2. Session Management: Supports managed sessions (PlanBan starts streaming runs) and external sessions (user interactive sessions can be attached), balancing coordination support and existing work habits.
  3. Worktree Integration: Creates an independent git worktree for each issue ticket, displays git status in real time, and solves the chaos of multi-task branch management.
  4. Deterministic State Transition: The clabby move command executes steps in a gated manner with overrides; failure of required steps blocks transitions, which can be fixed or resumed after recording the reason with clabby override, ensuring critical steps are not skipped.
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Section 04

Technical Implementation and Configuration Extensibility

Technical Implementation

PlanBan chooses Rust as the development language, with advantages including:

  1. Performance: Zero-cost abstractions ensure responsiveness under large-scale workflows;
  2. Reliability: The ownership system prevents null pointers and data races, suitable for handling git operations and external processes;
  3. Testability: Prioritizes black-box testing, tests compiled binaries, asserts exit codes and outputs, and engine rewrites do not require modifying tests.

The testing system covers: trycmd living document tests, assert_cmd black-box tests, and core engine e2e tests.

Configuration & Extensibility

Configuration is managed via clabby.toml, supporting directory discovery or explicit specification. The project provides examples/jira and examples/github examples, demonstrating that the same engine can adapt to different tracker data structures without code changes, proving the architectural extensibility.

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Section 05

Development Practices and Roadmap

Development Practices

PlanBan has established a strict CI process, including three checkpoints: cargo fmt --check, cargo clippy (-D warnings), cargo test. Developers can run the same checks locally via scripts (./scripts/ci.ps1/ci.sh) or configure git hooks to execute automatically, ensuring consistency between local and CI and reducing feedback loop time.

Roadmap

  • ✅ M1: Headless command center (synchronization, sessions, worktrees, overview, cron)
  • ✅ M2: Deterministic workflow engine (gated move/override with overrides)
  • M3: Tauri + React panel (drag-and-drop transitions, real-time sessions, Playwright end-to-end coverage)
  • Future: clabby init --from-jira (bootstrap configuration from an instance)
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Section 06

Summary: A New Paradigm for Human-Machine Collaboration

PlanBan represents a pragmatic human-machine collaboration concept: agents assist but do not replace human decision-making, tools enforce but do not rigidify processes. By combining a deterministic workflow engine with a flexible manual override mechanism, it provides teams with a reliable and practical agent coordination solution.

For teams exploring AI-assisted development, PlanBan offers a reference architectural pattern: treat agents as workflow steps rather than autonomous decision-makers, ensure quality through gated mechanisms, and retain the ability for manual intervention. This "human-in-the-loop" design philosophy is the most pragmatic AI integration strategy under current technical conditions.