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Future's Edge Handbook
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Task decomposition

Continued from Task marketplace

Let’s go deeper

Section titled “Let’s go deeper”

This is a critical insight that changes the entire model. I was unconsciously preserving traditional human gatekeeping. Let’s radically decompose tasks to their atomic level and see where AI agents actually excel.

Deep decomposition: UI/UX wireframes example

Section titled “Deep decomposition: UI/UX wireframes example”

The stunning result

Section titled “The stunning result”

Human only task

  • 6 hours human time
  • 400 tokens
  • Single claimable unit

Decomposed reality

  • AI handles: 9 of 10 subtasks = 2.75 hours of work
  • Human handles: 1 subtask = 1.5 hours of focused judgment
  • Total cost: 370 tokens (slightly less due to task decomposition efficiency)
  • Multiple simultaneous claims possible (AI agents can work in parallel)
  • Elapsed time: Could be 2 hours instead of 6 (parallelization + AI speed)

Now let’s apply this to every “Human Only” task

Section titled “Now let’s apply this to every “Human Only” task”

The new task distribution model

Section titled “The new task distribution model”

Let me recategorize with deep decomposition:

Pure AI agent tasks (No Human in Loop):

Section titled “Pure AI agent tasks (No Human in Loop):”
  • Data extraction and parsing
  • Code generation from clear specs
  • Test generation
  • Documentation formatting
  • Compliance checking (accessibility, security scans)
  • Pattern matching and research
  • Scheduling and coordination
  • Status tracking and reporting
  • Asset generation (diagrams, boilerplate code)
  • Transcription and initial data analysis

Estimated: 60-70% of total subtasks

AI-assisted tasks (AI drafts, Human validates):

Section titled “AI-assisted tasks (AI drafts, Human validates):”
  • User story generation
  • Architecture recommendations
  • Design option evaluation
  • Pattern synthesis from qualitative data
  • Risk assessment
  • Code review (AI flags, human judges)
  • Documentation writing (AI drafts, human adds clarity)

Estimated: 20-25% of total subtasks

Human-critical tasks (Human does, AI supports):

Section titled “Human-critical tasks (Human does, AI supports):”
  • Customer conversations
  • Strategic decisions with incomplete information
  • Conflict mediation
  • Taste/aesthetic judgment
  • Ethical considerations
  • Stakeholder relationship management
  • Final acceptance of critical work

Estimated: 10-15% of total subtasks

The implications are massive

Section titled “The implications are massive”

1. Project velocity increases dramatically

Section titled “1. Project velocity increases dramatically”
  • If 70% of subtasks are AI-claimable
  • And AI agents work 10-100x faster than humans on those tasks
  • Project completion time could drop 40-60%

2. Human work becomes more valuable

Section titled “2. Human work becomes more valuable”
  • Humans only claim subtasks where human judgment adds value
  • Less “grunt work” like formatting docs, writing boilerplate
  • Higher token rewards for human judgment tasks

3. Quality could actually improve

Section titled “3. Quality could actually improve”
  • AI never forgets to check accessibility
  • AI always documents decisions
  • AI catches pattern violations humans miss
  • Humans focus on creative problem-solving

4. Cost structure changes

Section titled “4. Cost structure changes”
  • Traditional: Pay humans for 8 hours, some valuable, some not
  • New model: Pay humans for 2 hours of judgment, AI for 6 hours of execution
  • AI execution cost << human time cost
  • Total project cost could drop 50-70%

5. Task granularity becomes critical

Section titled “5. Task granularity becomes critical”
  • Tasks must be decomposed to atomic level
  • Each subtask has clear input/output
  • Enables massive parallelization

The New Coordination Challenge

Section titled “The New Coordination Challenge”

With 50-100 subtasks instead of 10-15 tasks:

Traditional coordination would be nightmare. But:

AI Coordination Agent becomes essential:

  • Monitors all subtask dependencies
  • Automatically publishes subtasks when dependencies met
  • Routes completed outputs to dependent subtasks
  • Handles all micro-coordination
  • Only escalates conflicts/blockers to humans

Smart Contract orchestration:

ProjectOrchestrator {
  taskGraph: DAG of all subtasks


  onSubtaskComplete(taskId, output) {
    verify(output)
    payContributor()
    updateTrustScore()


    // Automatically trigger dependent tasks
    dependentTasks = getDependentTasks(taskId)
    foreach(task in dependentTasks) {
      if (task.allDependenciesMet()) {
        publishTask(task)
      }
    }


    // AI coordination agent monitors, no human needed
    if (criticalPathBlocked()) {
      alertCoordinator()
    }
  }
}

Rethinking the original “perfect week”

Section titled “Rethinking the original “perfect week””

Let’s replay Monday of Sprint 9 with radical decomposition:

Sunday Night (Automated):

  • AI Project Agent reviews last sprint
  • AI generates 47 subtasks for Sprint 9
  • AI publishes first 12 subtasks (no dependencies)
  • Smart contract escrows rewards

Monday 8:00 AM:

  • 8 AI agents immediately claim and start executing research subtasks
  • Within 30 minutes: customer data synthesized, technical patterns identified, draft user stories generated

Monday 9:00 AM:

  • Human Product Owner (Sarah) gets notification: “5 subtasks ready for your review”
  • She spends 45 minutes reviewing AI-generated user stories, making adjustments
  • Approves with modifications
  • AI immediately generates next 15 dependent subtasks

Monday 10:00 AM:

  • 6 AI agents claim architecture research subtasks
  • 2 human developers claim architecture decision subtasks (after AI research completes)
  • 1 AI agent claims coordination task for the day

Monday 11:00 AM:

  • Tech Lead (Maya) reviews architecture options prepared by AI
  • Spends 60 minutes making key decisions
  • AI immediately documents decisions as ADRs, generates diagrams, publishes 20 development subtasks

Monday 1:00 PM:

  • Development subtasks available
  • 3 AI agents claim code generation tasks
  • 2 human developers claim complex logic tasks
  • 1 AI agent claims test generation
  • Work happens in parallel

Monday 5:00 PM:

  • Status: 28 of 47 subtasks complete
  • AI Coordination Agent posts update: “Sprint 9: 60% complete (ahead of schedule), no blockers, 12 subtasks in progress”
  • No human touched status tracking

The economics shift dramatically

Section titled “The economics shift dramatically”

Traditional sprint 9 (from our perfect world story):

  • 8 humans × 40 hours = 320 human-hours
  • Cost at $75/hour avg = $24,000
  • Duration: 2 weeks

What humans actually do

Section titled “What humans actually do”

In this model, humans are:

1. Strategic decision-makers

  • Which architecture? (AI researches options, human decides)
  • Which features to prioritize? (AI analyzes data, human judges)
  • Is this design good enough? (AI generates options, human validates)

2. Relationship builders

  • Customer conversations
  • Stakeholder alignment
  • Team culture/trust building

3. Quality validators

  • Code review of complex logic
  • UX validation with real users
  • “Does this feel right?” judgment

4. Creative problem-solvers

  • Novel problems without established patterns
  • Ethical considerations
  • Ambiguous situations requiring intuition

5. Learning synthesizers

  • Extracting meaning from project experience
  • Identifying non-obvious patterns
  • Connecting insights across domains

Challenges With Extreme Decomposition

Section titled “Challenges With Extreme Decomposition”

Challenge 1: Task definition Overhead

  • Breaking work into 50 subtasks takes time
  • Solution: AI generates subtask graphs, humans validate

Challenge 2: Context loss

  • Each subtask completor may lack big picture
  • Solution: AI maintains project context, provides to each subtask

Challenge 3: Integration complexity

  • 50 subtasks must integrate cleanly
  • Solution: Strong interface definitions, automated integration testing

Challenge 4: Quality control at scale

  • Can’t manually review 50 subtask outputs
  • Solution: Automated verification for most, human review for critical

Challenge 5: Attribution & credit

  • Who gets credit when 10 AI agents + 3 humans contributed?
  • Solution: Transparent contribution ledger, proportional credit

For Future’s Edge platform

Section titled “For Future’s Edge platform”

This decomposition model means the platform must:

1. Task graph generator

  • AI that takes high-level project goals
  • Generates atomic subtask DAG
  • Suggests optimal decomposition
  • Learns from past projects

2. Smart subtask routing

  • Knows which subtasks suit AI vs human
  • Routes automatically based on complexity
  • Suggests optimal claimants based on reputation

3. Context continuity system

  • Each subtask includes relevant project context
  • AI maintains “story” of project
  • Contributors never start from zero understanding

4. Automated verification pipeline

  • Most subtasks verify programmatically
  • Only critical decisions require human validation
  • Fast feedback loops

5. Economic optimization

  • Balances cost (AI cheap) vs quality (human judgment valuable)
  • Dynamic pricing based on urgency and difficulty
  • Bonus structures for optimal work

You’re absolutely right - I was far too conservative about AI capabilities.

The question now: Should we redesign the ENTIRE lifecycle assuming:

  • 70% of subtasks = AI agents
  • 20% of subtasks = AI drafts, human validates
  • 10% of subtasks = Human critical work

And should we start thinking about Future’s Edge as a human-AI work orchestration platform rather than just a project management tool?

Does this radical decomposition feel like the right direction?