# Plan your implementation

Before you build your AI agent, plan carefully to ensure a successful implementation. Planning involves architectural decisions, tool selection, and workflow design. You should also [plan behavioral guidelines](https://www.infobip.com/docs/ai-agents/planning/plan-behavioral-guidelines) to define how the agent communicates and manages edge cases.

The following sections guide you through key planning decisions.

## Identify business outcomes

Before implementing agents, define the following:

- **Business outcomes** that you want to achieve. Example: reduce support time, increase conversion rate, improve customer satisfaction.
- **Specific tasks** that need to be performed. Example: book tickets, update records, schedule meetings.
- **KPIs** to measure success. Example: average handling time, first contact resolution, lead conversion, resolution rate.

**Examples:**

- **Support scenario**: Ticket classification, knowledge base lookup, status updates, escalation management
- **Sales scenario**: Lead qualification, opportunity creation, meeting scheduling, CRM updates

These business requirements determine whether an AI agent is required and the integrations that you need.

## Multi-agent system considerations

In a multi-agent system, multiple [AI agents](https://www.infobip.com/docs/ai-agents/get-started#ai-agent) work together to achieve the goal.

### When to use multi-agent system

Instead of building a large agent that manages everything, consider a multi-agent approach in the following cases:

- The agent needs to manage multiple distinct task categories.
- Different capabilities are independent or interact minimally.
- You want to keep complexity manageable as the system scales.

### Architecture components

- **[Orchestrator](https://www.infobip.com/docs/ai-agents/get-started#orchestrator) (Coordinator)**: Receives end user requests, identifies which sub-agent should manage them, and manages tool routing
- **[Sub-agents](https://www.infobip.com/docs/ai-agents/get-started#sub-agent)**: Each agent focuses on a specific domain
   Example:

  - Knowledge retrieval agent: Searches documentation, FAQs, knowledge bases
  - CRM update agent: Manages record creation and updates
  - User profile agent: Manages end user data and preferences
  - Scheduling agent: Manages appointments and calendar operations
  - Classification agent: Categorizes tickets, leads, or requests

This architecture keeps each sub-agent focused on its area of expertise, making the system easier to test, debug, and maintain.

## Plan the workflow

Follow this planning process before implementing AI agents:

1. **Decide if AI agent functionality is required**

   Decide whether the use case [requires AI agents](https://www.infobip.com/docs/ai-agents/get-started/when-to-use#when-to-use-ai-agents) or if [conversational AI](https://www.infobip.com/docs/ai-agents/get-started/when-to-use#when-to-use-conversational-ai) is sufficient.

2. **Define KPIs, business outcomes, and core tasks**

   Define the goal and identify specific outcomes that you want to achieve.

3. **Identify all use cases**

   List all scenarios where the agent will be used. Include both common interactions and unexpected situations.

4. **Assess architectural complexity**

   Decide between single-agent and multi-agent system.

5. **Identify the required [tools](https://www.infobip.com/docs/ai-agents/get-started#tools)**

   Identify both external integrations and internal systems that are required.

6. **Validate integration paths**

   Confirm how the agent will integrate with Answers and define escalation flows to human agents.

7. **Define behavioral guidelines, safety rules, and tone**

   Set clear boundaries for what the agent can and cannot do. For detailed guidance, refer to [Plan behavioral guidelines](https://www.infobip.com/docs/ai-agents/planning/plan-behavioral-guidelines). For best practices on writing effective prompts, refer to [Write prompts for AI agents](https://www.infobip.com/docs/ai-agents/advanced-topics/write-prompts).

8. **Identify test cases and evaluation criteria**

   Define how you will measure success before you start building the agent.

9. **Develop, test, and refine the agent**

   Refine the agent based on performance against your test cases.

10. **Deploy and maintain continuous evaluation**

    Monitor performance and refine the agent based on real-world usage.

## Next steps

After completing your planning:

- **Build your agent**: Follow the [Create and publish workflow](https://www.infobip.com/docs/ai-agents/build) to implement your planned design
- **Write effective prompts**: See [Write prompts for AI agents](https://www.infobip.com/docs/ai-agents/advanced-topics/write-prompts) to implement your behavioral guidelines