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Introduction

The Conflict Detector is a key tool in OVADARE, responsible for identifying conflicts within multi-agent workflows. It evaluates agent actions against defined policies to detect policy violations or misalignments.

By leveraging this tool, developers can ensure seamless collaboration among AI agents and prevent disruptions caused by conflicting actions.

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How It Works

The Conflict Detector monitors agent activities and evaluates them against predefined policies. When an action violates a policy, the Conflict Detector flags it as a conflict and stores it for further resolution.

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Key Features

• Policy Evaluation: Matches agent actions to the defined rules in the Policy Manager.
• Conflict Storage: Keeps track of identified conflicts for resolution or analysis.
• Customizable Detection Logic: Allows developers to adapt the detection mechanism to fit specific needs.

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Code Example

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Detecting a Conflict

Here’s how to use the Conflict Detector to identify a conflict:

from ovadare.conflicts.conflict_detector import ConflictDetector
from ovadare.policies.policy_manager import PolicyManager

# Initialize Policy Manager and Conflict Detector
policy_manager = PolicyManager()
conflict_detector = ConflictDetector(policy_manager=policy_manager)

# Define an agent action
action = {
    "agent_id": "Agent001",
    "action_type": "modify",
    "resource": "RestrictedFile"
}

# Detect conflicts
conflicts = conflict_detector.detect(agent_id="Agent001", action=action)

if conflicts:
    print("Conflicts detected:")
    for conflict in conflicts:
        print(conflict.to_dict())
else:
    print("No conflicts detected.")

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Retrieving All Conflicts

Use the following code to retrieve all stored conflicts:

conflict_list = conflict_detector.get_all_conflicts()
print(f"Total conflicts detected: {len(conflict_list)}")

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Resolving a Conflict

Conflicts can be resolved by removing them from the system:

if conflicts:
    conflict_id = conflicts[0].conflict_id
    conflict_detector.resolve_conflict(conflict_id=conflict_id)
    print(f"Conflict {conflict_id} resolved.")

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Example Use Case

Scenario: A data processing agent attempts to access a resource beyond its permissions.

• Step 1: The Conflict Detector identifies the action as a violation of the data access policy.
• Step 2: The conflict is stored and flagged for resolution.
• Step 3: A custom resolution strategy is applied to prevent unauthorized access.

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Best Practices

• Define Clear Policies: Ensure all agent actions are covered by comprehensive rules in the Policy Manager.
• Monitor Regularly: Use the get_all_conflicts method to keep track of active conflicts.
• Log Conflicts: Maintain a log of conflicts for future analysis and feedback-driven improvements.

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Learn More

Explore related topics:

• Resolution Engine: How conflicts are resolved.
• Define Custom Policies: Guide to creating effective policies.
• Integrate with AutoGen: Using OVADARE with multi-agent platforms.