---
title: "Agent Coordination"
description: "Multi-agent orchestration, monitoring, and conflict resolution for automated workflows"
---

# Agent Coordination

Agent Coordination in TracerTM enables multiple AI agents to work collaboratively on requirements traceability tasks while maintaining data consistency, preventing conflicts, and tracking performance metrics. The system provides comprehensive agent lifecycle management, real-time monitoring, and intelligent conflict resolution.

## Overview

TracerTM's agent coordination system provides:

- **Agent Registration**: Register and manage multiple agents working on projects
- **Activity Tracking**: Monitor all agent operations through event sourcing
- **Conflict Detection**: Automatically detect and resolve concurrent modifications
- **Performance Metrics**: Track efficiency, workload, and response times
- **Health Monitoring**: Real-time health checks and alerting
- **Resource Locking**: Prevent race conditions with distributed locks
- **Workload Distribution**: Intelligent task assignment and load balancing

## What are Agents in TracerTM?

Agents are autonomous entities (AI assistants, automation scripts, or external integrations) that interact with TracerTM programmatically. Each agent:

- Has a unique identity and type classification
- Belongs to one or more projects
- Generates events for all operations
- Can hold locks on items to prevent conflicts
- Tracks activity timestamps for health monitoring
- Stores custom metadata for extensibility

### Agent Types

TracerTM supports various agent types:

- **ai_agent**: AI assistants (Claude, GPT, Cursor, etc.)
- **automation**: Scheduled automation scripts
- **integration**: External tool integrations (Jira, GitHub, etc.)
- **cli**: Command-line interface operations
- **api_client**: Custom API consumers

## Core Components

### Agent Model

The Agent model represents a registered entity in the system:

```python
class Agent:
    id: str                    # Unique agent identifier (UUID)
    project_id: str           # Primary project
    name: str                 # Human-readable name
    agent_type: str           # Type classification
    status: str               # active, inactive, error
    agent_metadata: dict      # Custom properties
    last_activity_at: str     # ISO timestamp of last operation
```

### Agent Events

All agent operations generate events for auditability:

```python
class AgentEvent:
    id: str                   # Unique event identifier
    project_id: str          # Project context
    agent_id: str            # Agent that generated event
    event_type: str          # item_created, item_updated, conflict, etc.
    item_id: str             # Affected item (if applicable)
    event_data: dict         # Event-specific metadata
    created_at: datetime     # When event occurred
```

### Agent Locks

Prevent concurrent modifications with distributed locks:

```python
class AgentLock:
    id: str                  # Unique lock identifier
    project_id: str         # Project context
    item_id: str            # Locked item
    agent_id: str           # Lock holder
    lock_type: str          # exclusive, shared
    expires_at: datetime    # Automatic release time
```

## Agent Coordination Service

The `AgentCoordinationService` provides high-level coordination features:

### Agent Registration

Register a new agent in the system:

```python
from tracertm.services.agent_coordination_service import AgentCoordinationService

async with session:
    coordinator = AgentCoordinationService(session)

    agent = await coordinator.register_agent(
        project_id="proj-123",
        name="Claude Code Assistant",
        agent_type="ai_agent",
        metadata={
            "version": "4.5",
            "capabilities": ["code_review", "documentation"]
        }
    )
```

### Conflict Detection

Automatically detect when agents work on the same entities:

```python
# Detect conflicts in concurrent activity
conflicts = await coordinator.detect_conflicts(project_id="proj-123")

for conflict in conflicts:
    print(f"Conflict between {conflict.agent1_id} and {conflict.agent2_id}")
    print(f"Type: {conflict.conflict_type}")
    print(f"Entity: {conflict.entity_id}")
```

### Conflict Resolution

Resolve conflicts using configurable strategies:

```python
# Resolve using last-write-wins strategy
resolution = await coordinator.resolve_conflict(
    project_id="proj-123",
    conflict=conflict,
    strategy="last_write_wins"
)

print(f"Winner: {resolution.winner_agent_id}")
print(f"Strategy: {resolution.resolution_strategy}")
```

Available strategies:
- `last_write_wins`: Most recent modification wins
- `priority_based`: Use agent priority/metadata

### Activity History

Track all operations performed by an agent:

```python
# Get agent activity history
activity = await coordinator.get_agent_activity(
    agent_id="agent-456",
    limit=100
)

for event in activity:
    print(f"{event['timestamp']}: {event['event_type']} on {event['entity_type']}")
```

## Agent Monitoring

The `AgentMonitoringService` provides health checks and alerting:

### Health Checks

Monitor agent health status:

```python
from tracertm.services.agent_monitoring_service import AgentMonitoringService

monitor = AgentMonitoringService(session)

# Check health for all agents in a project
health_status = monitor.check_agent_health(
    project_id="proj-123",
    stale_threshold_hours=24.0
)

for status in health_status:
    print(f"{status['agent_name']}: {status['health']}")
    print(f"Last active: {status['hours_since_activity']} hours ago")
```

Health states:
- **healthy**: Active within 1 hour
- **idle**: Active within threshold but not recently
- **stale**: No activity beyond threshold
- **no_activity**: Never recorded activity
- **unknown**: Unable to determine state

### Alert Management

Get alerts for problematic agent behavior:

```python
# Get all alerts for a project
alerts = monitor.get_alerts(
    project_id="proj-123",
    alert_types=["stale", "high_conflict_rate", "error_rate"]
)

for alert in alerts:
    print(f"[{alert['severity']}] {alert['type']}: {alert['message']}")
```

Alert types:
- **stale_agent**: Agent inactive beyond threshold
- **high_conflict_rate**: Conflict rate exceeds 10%
- **error_rate**: High number of error events

## Agent Performance Metrics

The `AgentPerformanceService` analyzes agent efficiency and workload:

### Performance Statistics

Get detailed performance stats:

```python
from tracertm.services.agent_performance_service import AgentPerformanceService

performance = AgentPerformanceService(session)

# Get stats for specific agent
stats = await performance.get_agent_stats(
    agent_id="agent-456",
    time_window_hours=24
)

print(f"Total events: {stats['total_events']}")
print(f"Events per hour: {stats['events_per_hour']}")
print(f"Event type breakdown: {stats['event_types']}")
```

### Efficiency Scoring

Calculate agent efficiency (0-100 scale):

```python
efficiency = await performance.get_agent_efficiency(agent_id="agent-456")

print(f"Efficiency score: {efficiency['efficiency_score']}")
print(f"Rating: {efficiency['rating']}")  # Excellent, Good, Fair, Poor
print(f"Event diversity: {efficiency['event_type_diversity']}")
```

Efficiency calculation:
- Based on event frequency and diversity
- Score = (events × 10) + (event_types × 5), capped at 100
- Higher scores indicate more productive agents

### Workload Analysis

Assess agent workload levels:

```python
workload = await performance.get_agent_workload(agent_id="agent-456")

print(f"Workload: {workload['workload']}")  # Heavy, Moderate, Light, Idle
print(f"Events/hour: {workload['events_per_hour']}")
```

Workload classification:
- **Heavy**: >10 events/hour
- **Moderate**: 5-10 events/hour
- **Light**: 1-5 events/hour
- **Idle**: <1 event/hour

### Team Performance

Get aggregate metrics for all agents:

```python
team_stats = await performance.get_team_performance(project_id="proj-123")

print(f"Total agents: {team_stats['total_agents']}")
print(f"Total events: {team_stats['total_events']}")

for agent_stats in team_stats['agents']:
    print(f"{agent_stats['agent_name']}: {agent_stats['events_per_hour']} events/hour")
```

### Task Assignment Recommendations

Get intelligent agent recommendations for new tasks:

```python
recommendation = await performance.recommend_agent_assignment(
    project_id="proj-123",
    task_complexity="medium"
)

print(f"Recommended: {recommendation['agent_name']}")
print(f"Current workload: {recommendation['current_workload']} events/hour")
print(f"Reason: {recommendation['reason']}")
```

## Agent Metrics Service

The `AgentMetricsService` provides detailed performance metrics:

### Calculate Metrics

Get comprehensive metrics for time periods:

```python
from tracertm.services.agent_metrics_service import AgentMetricsService
from datetime import datetime, timedelta

metrics_service = AgentMetricsService(session)

# Calculate metrics for last 24 hours
since = datetime.utcnow() - timedelta(hours=24)
metrics = metrics_service.calculate_metrics(
    project_id="proj-123",
    agent_id="agent-456",  # Optional: specific agent
    since=since
)

for metric in metrics['metrics']:
    print(f"Agent: {metric['agent_name']}")
    print(f"Total ops: {metric['total_operations']}")
    print(f"Ops/hour: {metric['operations_per_hour']}")
    print(f"Success rate: {metric['success_rate']}%")
    print(f"Conflict rate: {metric['conflict_rate']}%")
    print(f"Avg response time: {metric['avg_response_time_ms']}ms")
```

### Workload by Status

Track items assigned to agents:

```python
workload = metrics_service.get_agent_workload(
    project_id="proj-123",
    agent_id="agent-456"
)

print(f"Total items: {workload['total_items']}")
print(f"By status: {workload['by_status']}")
# Example: {'draft': 5, 'in_progress': 10, 'done': 23}
```

## Using Agents via CLI

TracerTM provides CLI commands for agent operations:

### Python API Client

Use the `TraceRTMClient` for programmatic access:

```python
from tracertm.api.client import TraceRTMClient

# Initialize client with agent identity
client = TraceRTMClient()

# Register agent
agent_id = client.register_agent(
    name="Automation Script",
    agent_type="automation",
    metadata={"script_version": "1.0"},
    project_ids=["proj-123", "proj-456"]
)

# Get agent activity
activity = client.get_agent_activity(agent_id=agent_id, limit=50)

# Get items assigned to agent
items = client.get_assigned_items(agent_id=agent_id)

# Assign agent to additional projects
client.assign_agent_to_projects(
    agent_id=agent_id,
    project_ids=["proj-789"]
)

# Get all projects assigned to agent
projects = client.get_agent_projects(agent_id=agent_id)
```

### Event Logging

All operations are automatically logged:

```python
# Client automatically logs operations
item = client.create_item(
    title="New requirement",
    type="requirement",
    metadata={"priority": "high"}
)
# Generates: agent_operation event with action=create_item
```

## Agent Events and Logging

TracerTM uses event sourcing to track all agent operations:

### Event Types

Common agent event types:

- **agent_registered**: New agent registration
- **agent_operation**: General agent operation
- **item_created**: Agent created an item
- **item_updated**: Agent modified an item
- **item_deleted**: Agent deleted an item
- **conflict_detected**: Concurrent modification detected
- **conflict_resolved**: Conflict resolution completed
- **lock_acquired**: Agent acquired lock on item
- **lock_released**: Agent released lock
- **error**: Agent operation failed

### Event Data Structure

Events contain structured metadata:

```python
{
    "event_type": "item_updated",
    "entity_type": "item",
    "entity_id": "item-789",
    "agent_id": "agent-456",
    "data": {
        "action": "update_item",
        "changes": {"status": "in_progress"},
        "response_time_ms": 45,
        "timestamp": "2025-12-07T10:30:00Z"
    }
}
```

### Querying Events

Access event history through repositories:

```python
from tracertm.repositories.event_repository import EventRepository

events_repo = EventRepository(session)

# Get events by agent
agent_events = await events_repo.get_by_agent("agent-456")

# Get events by type
conflict_events = await events_repo.get_by_type(
    project_id="proj-123",
    event_type="conflict_detected"
)
```

## Multi-Agent Workflows

Coordinate multiple agents working together:

### Workflow Pattern: Code Review

```python
# Agent 1: Code reviewer
reviewer = await coordinator.register_agent(
    project_id="proj-123",
    name="Code Reviewer",
    agent_type="ai_agent",
    metadata={"role": "reviewer"}
)

# Agent 2: Documentation writer
doc_writer = await coordinator.register_agent(
    project_id="proj-123",
    name="Doc Writer",
    agent_type="ai_agent",
    metadata={"role": "documentation"}
)

# Reviewer processes code items
# Doc writer processes documentation items
# Coordination service detects conflicts if both touch same item
```

### Workflow Pattern: Import Pipeline

```python
# Agent 1: Jira importer
jira_agent = await coordinator.register_agent(
    project_id="proj-123",
    name="Jira Importer",
    agent_type="integration",
    metadata={"source": "jira"}
)

# Agent 2: GitHub importer
github_agent = await coordinator.register_agent(
    project_id="proj-123",
    name="GitHub Importer",
    agent_type="integration",
    metadata={"source": "github"}
)

# Both agents import concurrently
# Lock system prevents duplicate items
# Events track import progress
```

### Workflow Pattern: Validation Pipeline

```python
# Agent 1: Validator
validator = await coordinator.register_agent(
    project_id="proj-123",
    name="Validator",
    agent_type="automation",
    metadata={"role": "validation"}
)

# Agent 2: Enricher
enricher = await coordinator.register_agent(
    project_id="proj-123",
    name="Enricher",
    agent_type="automation",
    metadata={"role": "enrichment"}
)

# Validator checks data quality
# Enricher adds metadata and relationships
# Coordination prevents race conditions
```

## Best Practices

### Agent Identity

- **Use descriptive names**: Make agents easily identifiable
- **Set appropriate types**: Classify agents correctly for filtering
- **Store rich metadata**: Include version, capabilities, configuration
- **Update activity**: Keep `last_activity_at` current for health monitoring

### Conflict Prevention

- **Acquire locks**: Use locks for multi-step operations
- **Keep locks short**: Release locks quickly to prevent blocking
- **Handle lock failures**: Implement retry logic with exponential backoff
- **Use lock timeouts**: Set `expires_at` to auto-release stale locks

### Event Logging

- **Log all operations**: Maintain complete audit trail
- **Include context**: Add rich metadata to events
- **Use structured data**: Store JSON objects, not strings
- **Log timing**: Track response times for performance analysis

### Performance Optimization

- **Monitor workload**: Use metrics to balance task distribution
- **Set health thresholds**: Adjust stale thresholds based on agent type
- **Batch operations**: Reduce events by batching when possible
- **Clean up inactive agents**: Remove or deactivate stale agents

### Error Handling

- **Retry transient failures**: Use exponential backoff for network errors
- **Log errors as events**: Track failures for debugging
- **Set appropriate status**: Mark agents as "error" state when failing
- **Implement alerts**: Get notified of sustained errors

### Multi-Agent Coordination

- **Design for concurrency**: Assume multiple agents operate simultaneously
- **Use optimistic locking**: Detect conflicts and retry
- **Implement idempotency**: Operations should be safely repeatable
- **Monitor team metrics**: Track overall team efficiency

### Security Considerations

- **Validate agent identity**: Verify agents before granting access
- **Limit agent scope**: Restrict agents to assigned projects
- **Audit agent actions**: Review event logs regularly
- **Rotate credentials**: Update API keys periodically

## Integration Examples

### Claude Code Assistant

```python
# Register Claude agent
client = TraceRTMClient()
agent_id = client.register_agent(
    name="Claude Code",
    agent_type="ai_agent",
    metadata={
        "model": "claude-opus-4.5",
        "capabilities": ["code_review", "documentation", "testing"]
    }
)

# Claude creates requirements from code
item = client.create_item(
    title="User authentication system",
    type="requirement",
    metadata={"source": "code_analysis"}
)

# Claude links implementation
client.create_link(
    source_id="req-123",
    target_id="code-456",
    link_type="implements"
)
```

### GitHub Actions Automation

```python
# Register GitHub Actions agent
agent_id = client.register_agent(
    name="GitHub CI/CD",
    agent_type="automation",
    metadata={
        "workflow": "main.yml",
        "trigger": "push"
    }
)

# Update item status on deployment
client.update_item(
    item_id="item-789",
    status="deployed",
    metadata={"deployment_id": "deploy-123"}
)
```

### Cursor IDE Integration

```python
# Register Cursor agent
agent_id = client.register_agent(
    name="Cursor IDE",
    agent_type="ai_agent",
    metadata={
        "version": "2024.1.0",
        "workspace": "/project"
    }
)

# Cursor creates test coverage items
test_item = client.create_item(
    title="Unit tests for authentication",
    type="test",
    metadata={"coverage": "85%"}
)
```

## Related Documentation

- [Workflows](../02-workflows/): Understand item lifecycle states
- [Relationships](../04-relationships/): Link types and validation
- [Versioning](../06-versioning/): Track changes over time
- [Performance](../09-performance/): Optimize agent operations

## Summary

Agent Coordination in TracerTM provides:

- Complete agent lifecycle management
- Real-time conflict detection and resolution
- Comprehensive performance metrics and monitoring
- Event-based audit trail for all operations
- Distributed locking for data consistency
- Intelligent workload distribution
- Multi-agent workflow support

This enables teams to deploy multiple AI agents, automation scripts, and integrations that work harmoniously without data corruption or conflicts, while maintaining full visibility into system operations.
