Documentation Index
Fetch the complete documentation index at: https://docs.ovadare.com/llms.txt
Use this file to discover all available pages before exploring further.
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.
Steps for Real-Time Detection
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()
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)
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.")
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)
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"))
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.")
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.
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.
