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Industry Trend Analysis

This tutorial demonstrates how OVADARE helps orchestrate agent collaboration for analyzing industry trends while resolving conflicts effectively.

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Overview

In this scenario, a group of agents collaborates to:
  • Research Agent: Gathers data from industry publications.
  • Data Analyst Agent: Processes data to identify trends.
  • Strategy Planner Agent: Develops actionable insights based on trends.
  • Report Generator Agent: Compiles a final analysis report.

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Workflow

  1. Agents perform their tasks in parallel.
  2. Potential conflicts, such as redundant data usage or conflicting strategies, are detected.
  3. OVADARE resolves conflicts and ensures smooth collaboration.

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Steps for Conflict Detection and Resolution

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Step 1: Set Up OVADARE

Start by initializing OVADARE: 
from ovadare.conflicts.conflict_detector import ConflictDetector
from ovadare.resolutions.resolution_engine import ResolutionEngine
from ovadare.policies.policy_manager import PolicyManager

conflict_detector = ConflictDetector()
resolution_engine = ResolutionEngine()
policy_manager = PolicyManager()

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Step 2: Define Policies

Set boundaries to prevent redundant or unauthorized actions: 
policy_manager.add_policy({
    'name': 'DataAccessPolicy',
    'rules': {
        'resource_limit': 'unique_data_source',
        'access_level': 'authorized_only'
    }
})

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Step 3: Monitor Agent Activities

Capture actions performed by agents: 
agent_actions = [
    {'agent_id': 'research_agent', 'action': 'fetch_data', 'resource': 'industry_publication'},
    {'agent_id': 'data_analyst', 'action': 'process_data', 'resource': 'industry_publication'},
    {'agent_id': 'strategy_planner', 'action': 'develop_insight', 'conflict_with': 'data_analyst'},
    {'agent_id': 'report_generator', 'action': 'generate_report'}
]
conflicts = conflict_detector.detect(agent_actions)

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Step 4: Resolve Conflicts

Use the Resolution Engine to address conflicts: 
if conflicts:
    resolutions = resolution_engine.generate_resolutions(conflicts)
    for resolution in resolutions:
        print(f"Generated Resolution: {resolution}")

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Step 5: Enforce Resolutions

Communicate resolutions back to agents or platforms: 
for resolution in resolutions:
    agent_id = resolution['agent_id']
    corrective_action = resolution['corrective_action']
    autogen_api.apply_resolution(agent_id, corrective_action)

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Outcome

  • Agents efficiently analyze industry trends without redundancy.
  • Conflicts are detected early and resolved before impacting workflows.
  • Policies enforce structured collaboration.

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