Monitor Agent Conflicts

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Introduction

Monitoring agent conflicts is essential for understanding patterns, optimizing workflows, and improving system behavior. OVADARE offers built-in tools and APIs to track, analyze, and visualize conflicts across multi-agent platforms like AutoGen and CrewAI.

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Monitoring Workflow

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1. Enable Conflict Tracking

Use the ConflictDetector to capture conflicts dynamically during agent interactions:

from ovadare.conflicts.conflict_detector import ConflictDetector

conflict_detector = ConflictDetector()

# Example agent action
conflicts = conflict_detector.detect(
    agent_id="Agent001",
    action={"action": "access_data", "resource": "SensitiveDoc"}
)

if conflicts:
    print(f"Conflicts detected: {[conflict.to_dict() for conflict in conflicts]}")

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2. Log Conflicts

Store detected conflicts for future analysis. OVADARE provides persistent conflict storage for long-term monitoring:

conflict_log = conflict_detector.get_all_conflicts()

for conflict in conflict_log:
    print(f"Logged Conflict: {conflict.to_dict()}")

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3. Visualize Conflicts (Optional)

Integrate OVADARE with visualization tools to create dashboards or charts for better insights into agent conflicts:

import matplotlib.pyplot as plt

conflict_count = len(conflict_log)
labels = [conflict.related_agent_id for conflict in conflict_log]
values = [1 for _ in conflict_log]

plt.bar(labels, values)
plt.title("Conflict Frequency by Agent")
plt.xlabel("Agents")
plt.ylabel("Conflicts")
plt.show()

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4. Analyze Conflict Trends

Extract patterns from logged conflicts to identify recurring issues:

from collections import Counter

agent_conflict_count = Counter(conflict.related_agent_id for conflict in conflict_log)

print("Conflict frequency by agent:", agent_conflict_count)

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5. Use Monitoring APIs

Integrate OVADARE’s APIs for programmatic access to conflict data:

conflict = conflict_detector.get_conflict_by_id("Conflict001")

if conflict:
    print(f"Details of Conflict: {conflict.to_dict()}")
else:
    print("No conflict found with the given ID.")

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Example Use Case

Monitor conflicts in a multi-agent collaboration setup:

conflicts = conflict_detector.detect(
    agent_id="ResearchAgent",
    action={"action": "write_data", "resource": "SharedReport"}
)

if conflicts:
    for conflict in conflicts:
        print(f"Conflict detected: {conflict.to_dict()}")

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Tips for Effective Monitoring

• Use Dashboards: Combine OVADARE with tools like Grafana or Tableau for real-time conflict tracking.
• Set Alerts: Notify administrators when critical conflicts are detected using custom alert systems.
• Review Periodically: Regularly analyze conflict data to refine policies and agent behavior.

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Learn More

Explore related topics:

• Conflict Detection: Understand how OVADARE detects conflicts.
• Define Custom Policies: Improve monitoring by refining agent rules.
• Resolution Engine: Automate conflict resolution based on detected patterns.