Trace Agent

Overview

The Trace Agent is responsible for logging, monitoring, and analyzing the game's state changes, player actions, and system events. It provides essential debugging information and analytics capabilities.

Core Architecture

graph TD
    subgraph Trace Agent
        TA[Trace Agent] --> TM[Trace Manager]
        TA --> AM[Analytics Module]
    end

    subgraph Components
        AM --> DL[Data Logger]
        AM --> SA[Stats Analyzer]
        AM --> PM[Performance Monitor]
    end

    TA --> |Logs| Storage[Storage System]
    TA --> |Analyzes| State[Game State]

Key Components

Trace System

FeaturesImplementation

• Event Logging

  • State changes
  • Player actions
  • System events

• Analytics

  • Performance metrics
  • Player statistics
  • System health

• Monitoring

  • Real-time tracking
  • Error detection
  • Resource usage

class TraceAgent:
    async def log_event(
        self,
        event_type: str,
        data: Dict[str, Any],
        metadata: Optional[Dict] = None
    ) -> None:
        timestamp = datetime.utcnow().isoformat()

        trace_entry = TraceEntry(
            timestamp=timestamp,
            event_type=event_type,
            data=data,
            metadata=metadata or {}
        )

        await self.trace_manager.store_trace(trace_entry)

Analytics System

The Trace Agent processes data through multiple stages:

1. Data Collection
2. Event capture
3. State snapshots

4. Performance metrics

5. Analysis

6. Pattern detection
7. Trend analysis

8. Anomaly detection

9. Reporting

10. Stats generation
11. Alert triggering
12. Log aggregation

Trace Flow

sequenceDiagram
    participant AG as Game Agents
    participant TA as Trace Agent
    participant TM as Trace Manager
    participant ST as Storage

    AG->>TA: Log Event

    par Processing
        TA->>TA: Format Data
        TA->>TA: Add Metadata
        TA->>TA: Validate Entry
    end

    TA->>TM: Store Trace
    TM->>ST: Persist Data

    opt Analytics
        TA->>TA: Analyze Patterns
        TA->>TA: Generate Stats
    end

Best Practices

1. Logging Strategy
2. Structured logging
3. Context preservation

4. Performance impact

5. Data Management

6. Efficient storage
7. Data rotation

8. Privacy compliance

9. Analysis

10. Real-time processing
11. Pattern detection
12. Resource efficiency

Error Handling

The Trace Agent implements robust error handling:

try:
    # Log trace entry
    await self._log_entry(entry)

    # Process analytics if needed
    if entry.requires_analysis:
        await self._process_analytics(entry)

except TraceError as e:
    logger.error("Trace error: {}", str(e))
    # Use fallback logging
    await self._fallback_log(entry, error=str(e))
except Exception as e:
    logger.error("Unexpected error in trace: {}", str(e))
    # Ensure critical data is not lost
    await self._emergency_log(entry)

Performance Considerations

1. Logging Optimization
2. Batch processing
3. Async logging

4. Buffer management

5. Storage Strategy

6. Data compression
7. Index optimization

8. Cleanup policies

9. Resource Management

10. Memory efficiency
11. I/O optimization
12. CPU utilization

Integration Points

1. Story Graph
2. Workflow tracking
3. State transitions

4. Event logging

5. State Manager

6. State changes
7. History tracking

8. Checkpoint logging

9. Other Agents

10. Action logging
11. Decision tracking
12. Error reporting

Analytics Features

The Trace Agent provides various analytics capabilities:

class Analytics:
    async def analyze_patterns(self, timeframe: str) -> Dict[str, Any]:
        """Analyze patterns in traced data."""
        return await self._pattern_analysis(timeframe)

    async def generate_stats(self) -> Dict[str, Any]:
        """Generate statistical reports."""
        return await self._stats_generation()

    async def detect_anomalies(self) -> List[Anomaly]:
        """Detect anomalies in system behavior."""
        return await self._anomaly_detection()

Monitoring System

The agent includes a comprehensive monitoring system:

class Monitor:
    def __init__(self):
        self.metrics: Dict[str, Metric] = {}
        self.alerts: List[Alert] = []
        self.thresholds: Dict[str, float] = {}

    async def check_health(self) -> HealthStatus:
        """Check system health metrics."""
        return await self._health_check()

    async def trigger_alert(self, condition: str) -> None:
        """Trigger system alerts."""
        await self._alert_handling(condition)