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Introduction

Conflicts in multi-agent systems can arise when agents work at cross-purposes or violate defined policies. OVADARE helps detect these conflicts and provides resolutions to maintain harmony in workflows.

This guide demonstrates how to use OVADARE to identify and resolve conflicts in a multi-agent setup.

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Steps to Resolve Conflicts

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1. Initialize the Conflict Detector

The ConflictDetector component is responsible for detecting conflicts in agent workflows. Initialize it as follows:

from ovadare.conflicts.conflict_detector import ConflictDetector

conflict_detector = ConflictDetector()

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2. Register Policies and Agents

Ensure agents and their associated policies are registered before conflict detection begins.

  • Register Agents:
from ovadare.agents.agent import Agent

agent_a = Agent(agent_id="AgentA")
agent_b = Agent(agent_id="AgentB")
  • Assign Policies:
from ovadare.policies.policy import Policy
from ovadare.policies.policy_manager import PolicyManager

policy_manager = PolicyManager()

write_policy = Policy(
    name="WritePolicy",
    rules={"access_level": "write"}
)
policy_manager.add_policy(write_policy)

agent_a.assign_policy(write_policy)

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3. Detect Conflicts

Use the detect method of ConflictDetector to evaluate agent actions and identify conflicts:

action_a = {"action": "write_data", "resource": "Dataset1"}
conflicts = conflict_detector.detect(agent_id="AgentA", action=action_a)

if conflicts:
    for conflict in conflicts:
        print(f"Conflict detected: {conflict}")
else:
    print("No conflicts detected.")

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4. Generate Resolutions

The ResolutionEngine generates resolutions for detected conflicts. Initialize it and resolve conflicts:

from ovadare.conflicts.resolution_engine import ResolutionEngine

resolution_engine = ResolutionEngine()
resolutions = resolution_engine.generate_resolutions(conflicts)

for resolution in resolutions:
    print(f"Resolution generated: {resolution}")

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5. Apply Resolutions to Agents

Communicate the generated resolutions back to the agents:

resolution_engine.apply_resolutions(resolutions)

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Example Workflow

Here’s a complete example of a conflict resolution workflow:

# Initialize components
conflict_detector = ConflictDetector()
resolution_engine = ResolutionEngine()

# Define agents and policies
agent = Agent(agent_id="Agent001")
policy = Policy(name="ReadOnlyPolicy", rules={"access_level": "read-only"})
policy_manager.add_policy(policy)
agent.assign_policy(policy)

# Simulate an action that violates the policy
action = {"action": "write_data", "resource": "Dataset1"}
conflicts = conflict_detector.detect(agent_id="Agent001", action=action)

if conflicts:
    resolutions = resolution_engine.generate_resolutions(conflicts)
    resolution_engine.apply_resolutions(resolutions)

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Practical Tips

  • Predefine Policies: Ensure policies cover most potential conflict scenarios.
  • Test in Controlled Environments: Simulate agent workflows to identify conflicts before deploying in production.
  • Audit Agent Interactions: Regularly monitor agent activities for patterns that may indicate emerging conflicts.

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Next Steps

To learn more about how OVADARE handles conflict detection, visit Conflict Detection. For customizing resolutions, check out Customize Resolution Strategies.

Define Custom PoliciesDetect Conflicts Dynamically