DAG Execution

Establish efficient parallel execution of tasks using Directed Acyclic Graph (DAG) structures to maximize throughput while respecting dependencies.

What is DAG Execution?

A DAG (Directed Acyclic Graph) represents tasks as nodes and dependencies as directed edges. This structure enables:

• Parallel Execution: Independent tasks run simultaneously
• Dependency Respect: Tasks wait for prerequisites
• Optimal Ordering: Topological sort ensures valid execution
• Clear Visualization: Easy to understand task flow

DAG Structure

Node Definition

interface TaskNode {
  id: string;              // Unique task identifier
  name: string;            // Human-readable name
  agent: string;           // Assigned agent type
  dependencies: string[];  // IDs of prerequisite tasks
  level: number;           // Execution level (computed)
  status: 'pending' | 'running' | 'completed' | 'failed';
  priority: number;        // Higher = more important
  outputs?: any;           // Task results
}

Edge Definition

interface DependencyEdge {
  from: string;  // Source task ID
  to: string;    // Target task ID
  type: 'hard' | 'soft';  // hard = blocking, soft = preferred
}

Level Computation

Tasks are assigned execution levels based on dependencies:

Algorithm (Topological Sort)

1. Find all nodes with no dependencies → Level 0
2. Remove Level 0 nodes from graph
3. Find nodes whose dependencies are all assigned → Level 1
4. Repeat until all nodes assigned

Example

Task A (no deps)      → Level 0
Task B (no deps)      → Level 0
Task C (depends on A) → Level 1
Task D (depends on B) → Level 1
Task E (depends on C, D) → Level 2

Visual Representation

Level 0:  [Task A] ←→ [Task B]     Execute in parallel
              ↓           ↓
Level 1:  [Task C] ←→ [Task D]     Wait for L0, then parallel
              ↓           ↓
Level 2:      [Task E]              Wait for L1

Execution Strategy

Level-Based Parallel Execution

1. Initialize: Compute levels for all tasks
2. Execute Level 0: Launch all L0 tasks in parallel
3. Wait: Block until all L0 tasks complete
4. Proceed: Move to Level 1, repeat
5. Continue: Until all levels complete

Parallel Execution Rules

Rule	Description
Same Level	All tasks at same level can run in parallel
Cross Level	Tasks at Level N+1 wait for all Level N
Resource Limit	Max 13 concurrent agents
Priority	Higher priority tasks scheduled first

Failure Handling

When a task fails:

1. Mark Failed: Update task status
2. Cascade Check: Find dependent tasks
3. Block Dependents: Mark as blocked
4. Continue Others: Execute unaffected tasks
5. Recovery Option: Retry or manual intervention

Building a DAG

From Task List

tasks:
  - id: explore-codebase
    name: "Explore existing codebase"
    agent: code-explorer
    dependencies: []

  - id: gather-requirements
    name: "Gather requirements"
    agent: requirements-analyst
    dependencies: []

  - id: design-architecture
    name: "Design solution architecture"
    agent: architect-supreme
    dependencies: [explore-codebase, gather-requirements]

  - id: implement-core
    name: "Implement core functionality"
    agent: coder
    dependencies: [design-architecture]

  - id: implement-tests
    name: "Write test suite"
    agent: unit-tester
    dependencies: [design-architecture]

  - id: run-tests
    name: "Execute test suite"
    agent: test-runner
    dependencies: [implement-core, implement-tests]

Resulting Levels

Level 0: explore-codebase, gather-requirements
Level 1: design-architecture
Level 2: implement-core, implement-tests
Level 3: run-tests

DAG Validation

Before execution, validate the DAG:

Validation Checks

Check	Purpose
Cycle Detection	Ensure no circular dependencies
Orphan Detection	Find unreachable nodes
Dependency Exists	All referenced tasks exist
Agent Available	Required agents are defined

Cycle Detection Algorithm

1. Perform DFS from each node
2. Track visited nodes in current path
3. If revisit node in current path → CYCLE
4. If complete without revisit → VALID

Optimization Strategies

Critical Path Analysis

Identify the longest path through the DAG:

• These tasks determine minimum completion time
• Prioritize critical path tasks
• Allocate best resources to critical path

Load Balancing

Distribute work evenly across agents:

• Track agent workload
• Assign new tasks to least-loaded agent
• Consider task complexity in assignment

Speculative Execution

For soft dependencies:

• Start task before dependency completes
• Cancel if dependency fails
• Commit if both succeed

State Management

Task State Transitions

PENDING → RUNNING → COMPLETED
                  ↘ FAILED → RETRYING → COMPLETED
                                      ↘ BLOCKED

State Persistence

Checkpoint task states for recovery:

dag_checkpoint:
  timestamp: "2025-12-13T10:00:00Z"
  tasks:
    - id: "task-1"
      status: "completed"
      outputs: {...}
    - id: "task-2"
      status: "running"
      started_at: "2025-12-13T09:55:00Z"
    - id: "task-3"
      status: "pending"

Integration with Phases

DAG execution integrates with the 6-phase protocol:

Phase	DAG Role
EXPLORE	Build initial task graph
PLAN	Optimize and validate DAG
CODE	Execute implementation DAG
TEST	Execute test DAG
FIX	Dynamic DAG for fixes
DOCUMENT	Execute documentation DAG

Saga Pattern for Recovery

When tasks fail, use saga pattern for compensation:

Compensation Chain

Task A completes → Task B fails
                    ↓
         Compensate A (rollback)
                    ↓
         Return to known good state

Compensation Actions

Task Type	Compensation
File Create	Delete file
File Modify	Restore backup
Database Change	Rollback transaction
External API	Reverse API call

Additional Resources

Reference Files

• references/dag-algorithms.md

  - Detailed algorithms

• references/optimization.md

  - Advanced optimization techniques

Examples

• examples/sample-dag.json

  - Complete DAG example

• examples/execution-trace.json

  - Execution log example