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    melodic-software

    multi-agent-observability

    melodic-software/multi-agent-observability
    AI & ML
    19

    About

    SKILL.md

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    About

    Build observability interfaces for multi-agent systems. Use when monitoring multi-agent execution, tracking agent metrics, implementing logging for parallel agents, or debugging agent workflows.

    SKILL.md

    Multi-Agent Observability Skill

    Build observability interfaces for monitoring and measuring multi-agent systems.

    Purpose

    Guide the design and implementation of observability layers that provide real-time visibility into multi-agent execution.

    When to Use

    • Designing monitoring for agent fleets
    • Building metrics dashboards
    • Implementing logging architecture
    • Creating cost tracking systems

    Prerequisites

    • Understanding of the Three Pillars (@three-pillars-orchestration.md)
    • Familiarity with results-oriented patterns (@results-oriented-engineering.md)
    • Access to Claude Agent SDK documentation

    SDK Requirement

    Implementation Note: Full observability requires Claude Agent SDK with custom MCP tools and UI components. This skill provides design patterns.

    The Critical Principle

    "If you can't measure it, you can't improve it. If you can't measure it, you can't scale it."

    What to Observe

    Per-Agent Metrics

    Metric Purpose How to Track
    Status Know state Agent state enum
    Context usage Token consumption API response
    Cost Financial impact API usage data
    Tool calls What it's doing Hook logging
    Results Output verification Result parsing
    Duration Execution time Timestamps

    Aggregate Metrics

    Metric Purpose Calculation
    Total agents Scale Count active
    Total duration End-to-end time First to last
    Total cost Financial total Sum per-agent
    Success rate Reliability Success / total
    Coverage Scope Files touched

    Observability Components

    1. Agent Cards

    Real-time status for each agent:

    ┌─────────────────────────────────────┐
    │ scout_1                 [EXECUTING] │
    ├─────────────────────────────────────┤
    │ Template: scout-fast                │
    │ Model: haiku                        │
    │ Context: 12,500 / 100,000 tokens    │
    │ Cost: $0.05                         │
    │ Duration: 45s                       │
    │ Tool calls: 15                      │
    └─────────────────────────────────────┘
    

    Required fields:

    • Agent ID and template
    • Status (idle, executing, complete, error)
    • Model being used
    • Context usage (current / max)
    • Running cost
    • Execution duration
    • Tool call count

    2. Event Stream

    Real-time log of all activities:

    [10:30:00] scout_1 created (template: scout-fast)
    [10:30:01] scout_1 commanded: "Analyze auth module"
    [10:30:05] scout_1 Read: src/auth/login.ts
    [10:30:08] scout_1 Grep: "password" in src/auth/
    [10:30:15] scout_1 completed (duration: 14s)
    [10:30:16] scout_1 deleted
    

    Event types:

    • Agent lifecycle (create, delete)
    • Commands sent
    • Tool calls
    • Status changes
    • Errors

    3. Cost Tracking

    Track spend per agent and total:

    Cost Summary
    ────────────────────────────────────
    scout_1 (haiku)      $0.05
    scout_2 (haiku)      $0.04
    builder_1 (sonnet)   $0.35
    reviewer_1 (sonnet)  $0.12
    ────────────────────────────────────
    Total                $0.56
    Budget remaining     $4.44 (89%)
    

    Cost components:

    • Input tokens
    • Output tokens
    • Per-agent breakdown
    • Running total
    • Budget tracking

    4. Result Inspector

    View consumed and produced assets:

    Agent: builder_1
    
    Consumed Assets:
    ├── Scout report (summary)
    ├── src/auth/middleware.ts
    └── package.json
    
    Produced Assets:
    ├── src/auth/rate-limit.ts (created)
    ├── src/auth/middleware.ts (modified)
    └── tests/rate-limit.test.ts (created)
    
    Summary: "Implemented rate limiting middleware"
    Status: completed
    

    5. Log Viewer

    Filterable activity history:

    Filters: [agent: all] [level: all] [tool: all]
    
    10:30:00 INFO  scout_1   Created from template
    10:30:01 INFO  scout_1   Received command
    10:30:05 DEBUG scout_1   Read: src/auth/login.ts (1,200 tokens)
    10:30:08 DEBUG scout_1   Grep: found 5 matches
    10:30:12 WARN  scout_1   Context at 80% capacity
    10:30:15 INFO  scout_1   Completed successfully
    

    Implementation Patterns

    Logging Architecture

    # Event types
    class AgentEvent:
        timestamp: datetime
        agent_id: str
        event_type: str  # create, command, tool, status, error
        details: dict
    
    # Log collector
    def log_event(event: AgentEvent):
        # Store to database
        db.events.insert(event)
        # Emit to WebSocket
        ws.broadcast(event)
        # Update metrics
        metrics.update(event)
    

    Real-Time Updates

    # WebSocket for live updates
    async def agent_status_stream(agent_id):
        while agent_active(agent_id):
            status = get_agent_status(agent_id)
            yield status
            await asyncio.sleep(1)
    

    Cost Calculation

    def calculate_cost(usage):
        input_cost = usage.input_tokens * MODEL_INPUT_PRICE
        output_cost = usage.output_tokens * MODEL_OUTPUT_PRICE
        return input_cost + output_cost
    

    UI Components

    Minimal CLI View

    Orchestration: Add rate limiting
    ────────────────────────────────────
    Agents: 3 active | 2 complete | 0 error
    Cost: $0.56 / $5.00 budget
    Progress: ████████░░ 80%
    
    [scout_1] ✓ complete (14s)
    [scout_2] ✓ complete (12s)
    [builder] ⚡ executing (45s)
    

    Rich Dashboard View

    ┌─────────────────────────────────────────────────────────────┐
    │                    Orchestration Dashboard                    │
    ├─────────────────────────────────────────────────────────────┤
    │ Task: Add rate limiting to authentication                    │
    │ Started: 10:30:00 | Duration: 2m 15s | Cost: $0.56          │
    ├─────────────────────────────────────────────────────────────┤
    │ Agent Fleet                          │ Event Stream          │
    │ ┌─────────────────────────────────┐  │ [10:32:15] builder   │
    │ │ scout_1        [✓ complete]    │  │   Write: rate-limit  │
    │ │ scout_2        [✓ complete]    │  │ [10:32:10] builder   │
    │ │ builder        [⚡ executing]   │  │   Read: middleware   │
    │ │ reviewer       [○ pending]     │  │ [10:30:15] scout_2   │
    │ └─────────────────────────────────┘  │   completed          │
    ├─────────────────────────────────────────────────────────────┤
    │ Cost Breakdown     │ Results Summary                         │
    │ haiku:  $0.09     │ Files read: 8                           │
    │ sonnet: $0.47     │ Files written: 3                        │
    │ Total:  $0.56     │ Tests: 5/5 passing                      │
    └─────────────────────────────────────────────────────────────┘
    

    Design Checklist

    • Per-agent metrics defined
    • Aggregate metrics calculated
    • Event logging implemented
    • Real-time updates via WebSocket
    • Cost tracking per agent
    • Result inspection available
    • Log filtering supported
    • UI components designed

    Output Format

    When designing observability, provide:

    ## Observability Design
    
    ### Metrics
    
    **Per-Agent:**
    [List with tracking method]
    
    **Aggregate:**
    [List with calculation]
    
    ### Components
    
    **Agent Cards:** [fields and update frequency]
    **Event Stream:** [event types and storage]
    **Cost Tracking:** [breakdown and budgets]
    **Result Inspector:** [consumed/produced format]
    **Log Viewer:** [filters and retention]
    
    ### Implementation
    
    **Logging:** [architecture]
    **Real-Time:** [WebSocket design]
    **Storage:** [database schema]
    **UI:** [component specifications]
    

    Anti-Patterns

    Anti-Pattern Problem Solution
    No metrics Flying blind Track everything
    Delayed updates Stale status Real-time WebSocket
    No cost tracking Budget overruns Per-agent costs
    Missing logs Can't debug Log all events
    No aggregation Can't summarize Calculate totals

    Cross-References

    • @three-pillars-orchestration.md - Observability pillar
    • @results-oriented-engineering.md - Result patterns
    • @agent-lifecycle-crud.md - Agent state tracking
    • @orchestrator-design skill - System architecture

    Version History

    • v1.0.0 (2025-12-26): Initial release

    Last Updated

    Date: 2025-12-26 Model: claude-opus-4-5-20251101

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