> ## 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.

# Sales Strategy Workflow

> Leverage OVADARE to streamline the development of sales strategies by resolving conflicts between AI agents.

# Sales Strategy Workflow

This tutorial showcases how OVADARE can be utilized to manage multi-agent workflows for creating effective sales strategies.

## Overview

In this scenario, a team of agents is assigned to develop a sales strategy:

* **Market Analyst Agent**: Researches market trends and customer behavior.
* **Competitor Analyst Agent**: Analyzes competitor strategies and pricing.
* **Pricing Strategist Agent**: Develops pricing strategies based on market and competitor analysis.
* **Presentation Designer Agent**: Compiles the findings into a sales presentation.

### Workflow

1. Agents gather and analyze data.
2. They collaborate to develop a cohesive sales strategy.
3. Conflicts, such as data overlap or strategy contradictions, are detected and resolved.

***

## Detecting and Resolving Conflicts

### Step 1: Initializing OVADARE

Integrate OVADARE with the agent orchestration system:

```
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()
```

### Step 2: Define Policies

Set rules to govern agent behavior:

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

### Step 3: Monitoring Agent Actions

Track actions performed by agents:

```
agent_actions = [
    {'agent_id': 'market_analyst', 'action': 'analyze_market', 'resource': 'market_data'},
    {'agent_id': 'competitor_analyst', 'action': 'access_data', 'resource': 'competitor_pricing'},
    {'agent_id': 'pricing_strategist', 'action': 'create_pricing', 'strategy': 'below_market'}
]
conflicts = conflict_detector.detect(agent_actions)
```

### Step 4: Generating Resolutions

Resolve detected conflicts:

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

### Step 5: Applying Resolutions

Communicate resolutions back to agents:

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

***

## Outcome

With OVADARE:

* Agents collaborate effectively without violating defined policies.
* Sales strategy development proceeds without delays.
* Conflicts are detected early and resolved efficiently.

## Next Steps

* Learn how to define custom policies in [Define Custom Policies](../how-to-guides/define-custom-policies).
* Explore [Conflict Detection](../core-concepts/conflict-detection) for deeper insights.