Executive Summary

This white paper introduces the LLM Kanban system, a specialized middleware solution designed to enhance the capabilities of large language models (LLMs) in software development. By providing LLMs with a persistent Kanban-style board interface, LLM Kanban enables AI agents to overcome context window limitations, maintain awareness across sessions, and follow structured development workflows. The system acts as both an external memory and a structured project management framework, empowering LLMs to handle complex development tasks that would otherwise exceed their capabilities.

The Context, Limitations, and Memory Problem

LLMs face a fundamental constraint: limited context windows, which creates major challenges in complex development scenarios:

• Context windows are too small to hold extensive codebases or project histories.
•  Conversations reset when sessions end, causing “memory loss” about prior work. 
• Complex  tasks requiring multiple work sessions lose continuity.
• Maintaining global awareness within token limits is not feasible.

These limitations hinder LLM effectiveness in long-term, multi-step software development workflows.

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LLM Kanban: External Memory for Context Retention

The LLM Kanban server addresses these issues by providing a persistent, external system that preserves context across sessions.

• Card State Persistence: LLMs retrieve the current state of any card, including its description, tasks, comments, and history.
• Time Record Continuity: Track time spent on a task across multiple work sessions.
• Work Continuity: Even after starting a new chat, the LLM can pick up exactly where it left off.
• Knowledge Transfer: Team members can quickly understand project status at a glance.
• Living Documentation: The board becomes an evolving log of project progress and decision-making.

Task Development Workflow

LLM Kanban supports a structured, task-oriented development workflow, optimized for how LLMs operate:

1. Identify Next Task: LLM identifies the next card to work on from the backlog.
2. Start Work: Card is moved to "In Progress"; stopwatch starts.
3. Implement Tasks: LLM implements tasks one by one.
4. Document Progress: Adds comments detailing actions taken.
5. Complete Work: Stopwatch stops; card moves to "Testing".
6. Process Feedback: Human feedback is processed and implemented.
7. Finalize: Tasks are addressed or the card is moved to "Done".

This structure enables effective collaboration and continuity by the LLM while ensuring transparency with the human team members through the kanban board.

LLM-Driven Development with Human Review

In this workflow, the LLM does most of the implementation while humans focus on review and direction.

• Human: 
  
  • Creates high-level cards with basic requirements in the backlog.
• LLM:
  
  • Grooms cards into specific tasks.
  • Moves cards to “In Progress” and implements tasks.
  • Tracks time spent on implementation
  • Documents progress with detailed comments.
  • Moves cards to “Testing” upon completion.
• Human:
  
  • Reviews the implementation in testing 
  • Provides feedback via comments.
  • Approves or requests changes.
• LLM:
  
  • Addresses feedback by moving the card back to “In Progress” if needed.
  • Implements requested changes.
  • Once approved, moves card to “Done”.

This setup boosts developer productivity by offloading implementation details to the LLM while maintaining human quality control.

Human-Driven Development with LLM Support

Here, humans lead the implementation while LLMs provide support and analysis.

• LLM:
  
  • Grooms backlog by breaking down requirements into actionable tasks.
  • Suggests implementation approaches and technical strategies.
• Human:
  
  • Selects card to work on and moves  to "In Progress".
  • Uses stopwatches to track time spent on tasks
  • Implements the tasks..
  • Moves completed cards to "Testing".
• LLM:
  
  • Reviews the implementation.
  • Provides feedback on code quality, potential issues, and improvements.
  • Documents the review as comments.
• Human:
  
  • Addresses feedback and finalizes the work.

This model leverages LLMs for analysis and review, while humans retain hands-on control of the implementation.

Collaborative Grooming and Planning

In this scenario, humans and LLMs collaborate on shaping the development roadmap.

• Human: 
  
  • Adds initial ideas and requirements to the backlog.
• LLM:
  
  • Analyzes the codebase for context.
  • Breaks down cards  into specific tasks.
  • Identifies dependencies and challenges.
  • Suggests implementation strategies.
  • Estimates time requirements for tasks.
• Human:
  
  • Reviews and refines the groomed cards.
  • Prioritizes the backlog.
  • Assigns cards to LLM or human developers.
• Both: 
  
  • Implement tasks based on assignment.
    
    

Combining human domain knowledge with LLM technical analysis produces well-planned, high-quality output.

Time Tracking Capabilities

LLM Kanban server includes robust time-tracking features that enable both LLMs and humans to monitor and analyze time spent on tasks.

Stopwatch Functionality

• Start Tracking: Begins time monitoring on a specific card.
• Pause Tracking: Temporarily stops the timer.
• Resume Tracking: Continues from the paused state.
• Reset Timer: Clears time and starts fresh.
• Check Status: View current tracked time.

Time Management Strategies

1. Task Estimation: 
   
   • LLM can forecast time needs
   • Actual time tracking validates these estimates
   • Helps refine future estimates based on real data.
2. Productivity Analysis: 
   
   • Identify bottlenecks and inefficiencies.
   • Track patterns in the time usage across different task types
   • Recommend workflow optimizations based on the time data.
3. Focus Management: 
   
   • Promote time-boxed, single-task focus.
   • Provide visibility into context switching
4. Reporting: 
   
   • Generate reports for retrospectives and sprint analysis.
   • Provide insights into team velocity.
   • Support data-driven project management decisions

Task-Focused Development

LLM Kanban helps LLMs remain focused and productive with specific tasks.

• Clear Priorities: The board shows which tasks matter most.
• Structured Workflow: Enforces consistency from Backlog to Done.
• Progress Tracking: Ensures visibility into progress and accountability.
• Reduced Context Switching: Keeps LLMs locked into one task at a time.

Implementation Guidelines for LLMs

To maximize effectiveness, LLMs should:

• Check Board State First: Understand current status before acting.
• Follow the Workflow: Respect the kanban workflow (Backlog → In Progress → Testing → Done.)
• Document Actions: Leave comments for transparency.
• Break Down Work: Avoid tackling everything at once by creating small, focused tasks.
• Provide Context: Make cards understandable to humans.
• Respond to Feedback: Listen and adapt to human input.
• Track Time: Use the stopwatch for every task.

Business Benefits

Enhanced Development Efficiency

• Less Context Loading: Reduced time lost in re-orientation of project awareness.
• Continuous Progress: Persisted state across multiple sessions.
• Streamlined Workflows: Reduce friction and inefficiencies with standardized process.
• Clear Communication: Structured documentation of work and decisions.
• Effort Visibility: Understand how time is spent and required effort.

Improved Project Management

• Task Decomposition: Simplify complex work through manageable components.
• Progress Visibility: Clear tracking across all activities.
• Documentation Quality: A living, detailed record of work and decisions.
• Knowledge Retention: Context stays with the board, not just people.
• Structured Feedback: Formal, traceable review cycles and improvement.

Enhanced LLM Capabilities

• Extended Context: LLMs can effectively handle projects larger than their context windows.
• Consistent Workflows: Standardized process ensures reliable, reusable patterns.
• Focused Implementation: Better performance through clear task scope.
• Persistent Memory: Development history accessible across multiple sessions.
• Structured Collaboration: Clear mechanisms for human-LLM interaction
  
  

Conclusion

LLM Kanban  represents a significant advancement and leveraging LLM for software development. By providing a structured external memory system and workflow framework, it enables AI agents to overcome their inherent context  limitations, and effectively participate in complex development projects.

 The system's support for multiple collaboration models - from LLM-driven  development with human review to human implementation with LLM support - create flexibility for teams to find the optimal balance based on their specific needs and preferences.

 As AI capabilities continue to evolve, frameworks like LLM Kanban that address the fundamental limitations of context, windows and session persistence will become increasingly valuable tools for maximizing the effectiveness of AI in software development.

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