> ## 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.

# Meeting Preparation with AutoGen

> Learn how to use OVADARE to manage conflicts and resolutions in a multi-agent workflow designed for meeting preparation.

This tutorial demonstrates how to integrate OVADARE with AutoGen to orchestrate multi-agent workflows for preparing meetings while detecting and resolving conflicts.

## Overview

Dave is preparing for an important sales meeting and sets up multiple agents in AutoGen:

* **Lead Researcher Agent**: Conducts research on the target company.

* **Product Specialist Agent**: Analyzes product fit for the target.

* **Sales Strategist Agent**: Develops a tailored sales strategy.

* **Briefing Coordinator Agent**: Compiles a detailed meeting briefing.

### Workflow

The agents are tasked to:

1. Research the target company's background and industry trends.

2. Analyze product fit based on the research.

3. Develop a comprehensive sales strategy.

4. Compile all findings into a detailed briefing document.

Outputs include:

* Research reports.

* Industry analysis.

* Sales strategies.

* Final briefing documents.

***

## Detecting Conflicts with OVADARE

In this scenario, conflicts might arise if:

* Agents compete for overlapping resources.

* Strategies contradict each other.

* Policies regarding sensitive data are violated.

### Step 1: Setting Up OVADARE

First, ensure OVADARE is integrated with AutoGen:

```
from ovadare.conflicts.conflict_detector import ConflictDetector
from ovadare.policies.policy_manager import PolicyManager
from ovadare.resolutions.resolution_engine import ResolutionEngine

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

### Step 2: Define Policies

Define policies to govern agent actions:

```
policy_manager.add_policy({
    'name': 'DataSensitivityPolicy',
    'rules': {
        'access_level': 'restricted',
        'resource': 'confidential_documents'
    }
})
```

### Step 3: Monitor Agent Actions

As the agents in AutoGen execute tasks, OVADARE listens for activities:

```
agent_actions = [
    {'agent_id': 'lead_researcher', 'action': 'access_data', 'resource': 'confidential_documents'},
    {'agent_id': 'sales_strategist', 'action': 'create_strategy', 'strategy': 'aggressive_pricing'}
]
conflicts = conflict_detector.detect(agent_actions)
```

### Step 4: Resolving Conflicts

If conflicts are detected, OVADARE’s Resolution Engine generates recommendations:

```
if conflicts:
    resolutions = resolution_engine.generate_resolutions(conflicts)
    for resolution in resolutions:
        print(f"Resolution: {resolution}")
```

### Step 5: Applying Resolutions in AutoGen

Send the resolutions back to AutoGen agents:

```
for resolution in resolutions:
    agent_id = resolution['agent_id']
    action = resolution['corrective_action']
    autogen_api.apply_resolution(agent_id, action)
```

***

## Outcome

By using OVADARE, Dave ensures:

* Agents collaborate without policy violations.

* Conflicts are resolved before disrupting workflows.

* The meeting preparation process runs smoothly, resulting in a well-prepared team.

## Next Steps

* Learn more about integrating OVADARE with AutoGen in [Integrate OVADARE with AutoGen](../how-to-guides/integrate-with-autogen).

* Explore [Custom Policies](../how-to-guides/define-custom-policies) for advanced scenarios.