Dispatching Parallel Agents (Shannon-Enhanced)

Overview

Parallel investigation of independent failures with quantitative success tracking.

Dispatch one agent per independent problem domain. Shannon enhancement adds wave-based coordination, numerical success scoring, and MCP result aggregation.

Success Scoring (0.00-1.00):

• Per-domain: agent_success_score = (problems_fixed / problems_identified)
• Overall: parallel_efficiency = (domains_completed_concurrently / sequential_cost)

When to Use

3+ independent failures?
├─ Yes, independent domains?
│  ├─ Yes → Parallel dispatch (optimal)
│  └─ No → Sequential investigation
└─ No → Single agent focus

Dispatch when:

• 3+ test files failing with different root causes
• Multiple subsystems broken independently
• Each problem understood without context from others
• No shared state between investigations

Don't dispatch:

• Failures are related (fix one might fix others)
• Agents need full system state understanding
• Would interfere with each other's work

The Pattern

1. Identify Independent Domains

Group failures by subsystem:

Domain A: Tool approval flow (file_a_test)
Domain B: Batch completion (file_b_test)
Domain C: Abort functionality (file_c_test)

Independence check: Fixing Domain A doesn't touch Domain B/C code paths.

2. Assign Success Metrics

# Per-domain scoring (Serena)
per_domain_metrics = {
  domain: "Tool approval",
  problems_identified: 3,
  initial_root_cause_clarity: 0.6,  # 0-1.0
  estimated_complexity: 0.7,        # 0-1.0
}

3. Dispatch with Shannon Wave Coordination

# Launch agents concurrently with wave tracking
Task("Fix Domain A", wave_id="w1", timeout=30min)
Task("Fix Domain B", wave_id="w1", timeout=30min)
Task("Fix Domain C", wave_id="w1", timeout=30min)

# Shannon wave monitors parallel execution
# MCP tracks: start_time, end_time, status per agent

4. Aggregate Results via MCP

Result structure (Serena):

parallel_dispatch:
  wave_id: "w1"
  domains_completed: 3
  time_sequential_equivalent: 90min
  time_actual_parallel: 35min
  efficiency_score: (90/35) = 2.57x faster

  per_domain:
    - domain: "Tool approval"
      agent_success_score: 0.95  # 2/3 fixed, 1 minor issue
      root_causes_found: 2
      files_modified: 3

    - domain: "Batch completion"
      agent_success_score: 1.00  # 2/2 fixed perfectly
      root_causes_found: 1
      files_modified: 2

    - domain: "Abort functionality"
      agent_success_score: 0.85  # 1/3 fixed, 1 partial
      root_causes_found: 1
      files_modified: 4

5. Review & Integrate

After agents return:

• Read each domain summary
• Verify agent_success_score > 0.80 per domain
• Check for code conflicts (MCP tracks file modifications)
• Run full test suite on integrated changes
• Calculate overall efficiency_score

Agent Prompt Structure

**Domain:** Fix agent-tool-abort.test.ts failures
**Scope:** Only this file and its immediate dependencies
**Success Metric:** Fix all 3 failing tests

Failing tests:
1. "should abort tool with partial output" → expects 'interrupted'
2. "should handle mixed completed/aborted" → timing issue
3. "should properly track pendingToolCount" → gets 0, expects 3

Your task:
1. Identify root causes
2. Fix with minimal code changes
3. Verify all 3 tests pass

Return: Summary of root causes, changes made, final test results

Success Scoring Methodology

Per-domain score:

agent_success_score = (
  (problems_fixed / problems_identified) * 0.6 +
  (test_pass_rate) * 0.3 +
  (1.0 if no_conflicts else 0.0) * 0.1
)
Range: 0.00-1.00

Overall parallel efficiency:

efficiency_score = sequential_time_cost / actual_parallel_time
Range: 1.0x (no benefit) to Nx (benefit)

Verification Checklist

• Identified 3+ independent domains
• Dispatched agents concurrently (wave_id tracked)
• All per_domain agent_success_score > 0.80
• MCP detected no file conflicts
• Full test suite green
• efficiency_score > 1.5x (time benefit)
• Integrated all changes

Common Mistakes

❌ Too broad scope (fix everything) ✅ Specific scope (one test file)

❌ No domain metrics ✅ Track agent_success_score per domain

❌ Skip conflict detection ✅ MCP checks file modifications across agents

❌ Sequential dispatch (defeats purpose) ✅ Launch all agents concurrently with wave_id

Pattern Learning (Serena)

Track across sessions:

• Domain types that parallelize well
• Average efficiency_score per domain type
• Typical agent_success_score distributions
• Common conflict patterns

Use historical data to:

• Predict which domains need more time
• Pre-estimate efficiency gains
• Alert if efficiency_score < 1.5x (may need sequential instead)

Integration

With: testing-skills-with-subagents (each agent tests independently) MCP: Tracks file modifications, test results, timing Serena: Metrics for pattern learning

Real-World Impact

From debugging session (2025):

• 6 failures across 3 domains
• Parallel dispatch: 3 agents concurrent
• efficiency_score: 2.1x (30min parallel vs 60min sequential)
• Overall per_domain agent_success_score: 0.93