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Epic 5, Story 0, Task 31: Multi-Agent Coordination Feasibility Investigation

Status: TODO
Priority: C (Could Have)
Last updated: 2026-01-14 (v0.5.1.31+0 – Task created)
Started: [TBD]
Completed: [TBD]
Version: v0.5.1.31+0
Code: E5S00T31

Task ID​

Format: E\{epic\}:S\{story\}:T\{task\}

Full Task ID: E5:S01:T31

Repository Pattern: FR-031 = E5:S01:T31 (abstract space: v0.5.1.31+0)

Scope​

Investigate the feasibility of incorporating multi-agent coordination patterns (planners/workers architecture, hierarchical task distribution, long-running autonomous agents) into ai-dev-kit workflows and frameworks, based on research from Cursor's scaling experiments with hundreds of concurrent agents.

Problem Statement:

  • Current workflows (RW, UKW, etc.) run sequentially with a single agent
  • Complex tasks can't be broken down and executed in parallel
  • No task specialization - same agent handles planning, execution, and validation
  • Difficult to scale workflows to handle very large projects or multiple concurrent tasks

Solution:

  • Research multi-agent coordination patterns from Cursor's experiments
  • Evaluate planner/worker architecture, coordination mechanisms, model selection
  • Assess feasibility of integrating multi-agent patterns into existing workflows
  • Create feasibility assessment document with recommendations

Input​

Deliverable​

Feasibility Investigation Document covering:

  1. Multi-Agent Architecture Patterns Analysis

    • Planner/worker separation evaluation
    • Hierarchical task distribution patterns
    • Role specialization strategies

  2. Coordination Mechanisms Research

    • Task distribution mechanisms
    • Conflict resolution strategies
    • State management approaches

  3. Model Selection Strategies

    • Model characteristics for different roles (planners vs workers vs judges)
    • Long-running task capabilities
    • Extended autonomous work requirements

  4. Prompt Engineering Patterns

    • Effective prompting strategies for multi-agent coordination
    • Patterns to prevent pathological behaviors
    • Focus maintenance over long periods

  5. Scalability Analysis

    • Limits and bottlenecks when scaling to dozens or hundreds of agents
    • Performance overhead assessment
    • Reliability considerations

  6. Integration Points Identification

    • Specific integration points in RW, UKW, PVW, CMW
    • Risk/benefit analysis for each integration point
    • Implementation complexity assessment

  7. Feasibility Assessment

    • Clear recommendations (proceed/not proceed/modified approach)
    • Evidence-based rationale
    • Implementation roadmap (if feasible)

Acceptance Criteria​

  • Criterion 1: Comprehensive research document completed covering all key research questions
  • Criterion 2: Feasibility assessment completed with clear recommendations (proceed/not proceed/modified approach)
  • Criterion 3: Integration points identified for each existing workflow (RW, UKW, PVW, CMW)
  • Criterion 4: Coordination mechanism patterns documented with pros/cons analysis
  • Criterion 5: Model selection recommendations provided for different agent roles
  • Criterion 6: Prompt engineering patterns documented with examples
  • Criterion 7: Scalability analysis completed with identified limits and bottlenecks
  • Criterion 8: Implementation roadmap created (if feasibility assessment is positive)

Approach​

  1. Literature Review

    • Analyze source material (Cursor blog post)
    • Review related research on multi-agent coordination
    • Document key insights and patterns

  2. Architecture Analysis

    • Review existing workflow implementations (RW, UKW, PVW, CMW)
    • Identify current coordination mechanisms
    • Assess current limitations and bottlenecks

  3. Pattern Identification

    • Map multi-agent patterns to ai-dev-kit workflows
    • Identify specific integration points
    • Evaluate pattern applicability

  4. Feasibility Assessment

    • Evaluate technical feasibility
    • Assess practical feasibility (resource requirements)
    • Consider compatibility with existing infrastructure

  5. Recommendation

    • Provide clear go/no-go decision with rationale
    • Create implementation roadmap (if positive)
    • Document risks and mitigation strategies

Dependencies​

Depends On:

  • Source material availability (✅ Available)
  • Existing workflow documentation (✅ Available)
  • Framework infrastructure understanding (✅ Available)

Blocks:

  • Potential future FRs for multi-agent workflow implementations (if investigation is positive)

Blocked By:

  • None (investigation phase)

Parallel Development Candidacy: Safe - Investigation work can proceed independently

Related BR/FR Links:

Related Articles:

Related Workflows:

  • Release Workflow (RW) - Potential integration point
  • Update Kanban Workflow (UKW) - Potential integration point
  • Package Version Workflow (PVW) - Potential integration point
  • Changelog Management Workflow (CMW) - Potential integration point

Version Anchor​

Forensic Marker Format: ✅ COMPLETE (vRC.E.S.T+B) (e.g., ✅ COMPLETE (v0.5.1.31+1))

When Task is Complete:

  • Add forensic marker to Task document
  • Update Story document Task Checklist
  • Update FR-031 intake decision section
  • Create release version marker

Notes​

Investigation Focus Areas:

  1. Planner/Worker Architecture

    • How does planner/worker separation improve coordination vs flat structure?
    • What makes a good planner agent vs a good worker agent?
    • How do sub-planners work for recursive task decomposition?
    • What's the optimal ratio of planners to workers?

  2. Coordination Mechanisms

    • Why did locking mechanisms fail? (held too long, bottlenecks, brittleness)
    • How does optimistic concurrency control work for agents?
    • What coordination mechanisms avoid bottlenecks while preventing conflicts?
    • How do agents handle shared state and task queues?

  3. Model Selection

    • Which models work best for planners vs workers vs judges?
    • How do different models handle long-running tasks?
    • What model characteristics matter for extended autonomous work?

  4. Prompt Engineering

    • What prompt patterns enable good coordination?
    • How do prompts prevent pathological behaviors?
    • What prompts maintain focus over long periods?

  5. Simplicity Principles

    • What complexity can be removed rather than added?
    • How do we avoid over-engineering coordination?
    • What's the minimal viable coordination mechanism?

  6. Long-Running Capabilities

    • How do agents maintain context over days/weeks?
    • What mechanisms prevent drift and tunnel vision?
    • How do periodic fresh starts work?

Key Principles to Apply:

  • Simplicity First: Remove complexity rather than add it
  • Right Amount of Structure: Balance between too little and too much
  • Model Selection: Use best model for each role
  • Prompt Engineering: Invest in effective prompts
  • Periodic Fresh Starts: Combat drift and tunnel vision

This task is part of the Kanban Framework. See packages/frameworks/kanban/ for complete framework documentation.