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Introduction

Dynamic conflict detection ensures that multi-agent systems operate without breakdowns by identifying issues in real-time. OVADARE integrates with platforms like AutoGen to monitor and analyze agent activities continuously, offering immediate conflict detection.

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Steps for Real-Time Detection

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1. Initialize the Conflict Detector

The ConflictDetector evaluates agent actions against defined policies to detect conflicts dynamically:

from ovadare.conflicts.conflict_detector import ConflictDetector

conflict_detector = ConflictDetector()

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2. Integrate with the Event System

Connect OVADARE to the platform’s event system or activity stream. For example, with AutoGen:

from autogen.platform import EventStream

# Subscribe to agent activity events
event_stream = EventStream()
conflict_detector.subscribe(event_stream)

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3. Handle Agent Activities

OVADARE processes incoming actions and checks them for potential conflicts using the

detect` method:

`def handle_agent_action(agent_id, action):
    conflicts = conflict_detector.detect(agent_id=agent_id, action=action)
    if conflicts:
        for conflict in conflicts:
            print(f"Conflict detected: {conflict.to_dict()}")
    else:
        print("No conflicts detected.")

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4. Resolve Detected Conflicts in Real Time

When conflicts are identified, use the ResolutionEngine to generate and apply resolutions dynamically:

from ovadare.conflicts.resolution_engine import ResolutionEngine

resolution_engine = ResolutionEngine()

# Detect and resolve conflicts
conflicts = conflict_detector.detect(agent_id="AgentA", action={"action": "write_data", "resource": "Dataset1"})
if conflicts:
    resolutions = resolution_engine.generate_resolutions(conflicts)
    resolution_engine.apply_resolutions(resolutions)

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5. Logging and Monitoring

Configure logging to track conflict activity in real-time and integrate with monitoring tools for better visibility:

import logging

logging.basicConfig(level=logging.INFO)

# Attach a logger for monitoring
conflict_detector.set_logger(logging.getLogger("ConflictLogger"))

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Example Workflow

Here’s a step-by-step demonstration of how OVADARE detects conflicts dynamically:

# Initialize conflict detection
conflict_detector = ConflictDetector()

# Simulate incoming agent actions
actions = [
    {"agent_id": "Agent001", "action": "read_data", "resource": "Document1"},
    {"agent_id": "Agent002", "action": "write_data", "resource": "Document1"}
]

for action in actions:
    conflicts = conflict_detector.detect(agent_id=action["agent_id"], action=action)
    if conflicts:
        print(f"Conflicts detected: {[conflict.to_dict() for conflict in conflicts]}")
    else:
        print("No conflicts detected.")

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Practical Tips

  • Event Stream Integration: Use event-driven mechanisms from platforms like AutoGen for seamless agent action capture.
  • Policy Optimization: Ensure policies are optimized for real-time evaluation to prevent performance bottlenecks.
  • Testing: Test dynamic detection workflows extensively before deploying in production.

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Learn More

To dive deeper into OVADARE’s integration capabilities, check out Integrate OVADARE with AutoGen. Explore Resolve Multi-Agent Conflicts for advanced conflict resolution techniques.

Resolve Multi-Agent ConflictsCustomize Resolution Strategies