1
2
3
4
5
6
7
8
9
10
11
┌─────────────────────────────────────────────────────────────┐
│                    PM-AGENT ARCHITECTURE                    │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Knowledge Base → Task Assignment → Sentry Evaluation      │
│         ↓                ↓                  ↓               │
│  Context          Capability Match    Stage Progression    │
│  Domain           Workload Balance    Task Activation      │
│  History          Authority Check                          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Configuration

Basic Setup

1
2
3
4
5
6
7
8
9
10
pm_agent:
  id: pm-agent-001
  knowledge_base: case-context
  assignment_rules:
    - capability_match
    - workload_balance
    - authority_check
  sentry_evaluation:
    frequency: on_task_complete
    auto_activate: true

Knowledge Base Integration

1
2
3
4
5
6
7
knowledge_base:
  context: ml-frameworks-research
  sources:
    - domain_concepts: ml-frameworks
    - past_cases: [case-001, case-002]
    - member_profiles: [researcher-001, ai-specialist-g3]
  refresh: on_case_update

Task Assignment

Assignment Logic

1
2
3
4
5
6
7
1. Identify available tasks (sentries satisfied)
2. Extract task requirements
3. Match to member capabilities
4. Check workload capacity
5. Verify authority rules (SDS-031)
6. Assign to qualified member
7. Log assignment to IFL

Assignment Rules

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
assignment_rules:
  - name: capability_match
    priority: 1
    logic: |
      Match task.required_capabilities to member.capabilities
      Score: Jaccard similarity
      Threshold: >= 0.7
  
  - name: workload_balance
    priority: 2
    logic: |
      Calculate: member.active_tasks / member.capacity
      Prefer: Lowest utilization
      Max: 0.8 (80% capacity)
  
  - name: authority_check
    priority: 3
    logic: |
      If task.privileged_action:
        Verify: member.authority_level >= required_level
        Else: Block assignment

Sentry Evaluation

Evaluation Process

1
2
3
4
5
1. Collect sentry conditions
2. Evaluate each condition (boolean)
3. Combine with logic operator (AND/OR)
4. Return: satisfied | not_satisfied
5. If satisfied: Trigger action

Example Sentry

1
2
3
4
5
6
7
8
9
10
sentry:
  id: sentry-analysis-entry
  type: entry
  stage: analysis
  conditions:
    - data_collected: true
    - quality_score: >= 0.8
    - peer_review: passed
  logic: AND
  action: activate_stage

Discretionary Task Activation

Activation Logic

1
2
3
4
5
6
7
8
9
10
11
discretionary_task:
  id: task-additional-data
  activation_sentry:
    conditions:
      - sample_size: < 100
      - confidence_interval: > 0.1
    logic: OR
  pm_agent_action:
    - evaluate_sentry
    - if_satisfied: assign_to_qualified_member
    - log_activation_to_ifl

Monitoring

PM-Agent Logs

1
2
3
4
5
6
7
8
9
10
11
# View assignment decisions
sea pm-agent logs --case case-research-001

# Output:
# [2026-01-04 21:00:00] Task: literature-search
#   Assigned to: literature-summarizer-g3
#   Reason: Capability match (0.95), Low workload (0.3)
#
# [2026-01-04 21:15:00] Task: hypothesis-draft
#   Assigned to: researcher-001
#   Reason: Requires human judgment

Metrics

1
2
3
4
5
pm_agent_metrics:
  assignment_accuracy: 0.92  # Correct assignments
  sentry_evaluation_latency: 50ms  # Avg evaluation time
  task_completion_rate: 0.88  # Tasks completed successfully
  manual_override_rate: 0.05  # Human overrides

PM-Agent Orchestration

PM-Agent Architecture