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 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 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 (Coordinator): Receives end user requests, identifies which sub-agent should manage them, and manages tool routing

• Sub-agents: 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 or if 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

   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. For best practices on writing effective prompts, refer to Write prompts for AI agents.

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 to implement your planned design
• Write effective prompts: See Write prompts for AI agents to implement your behavioral guidelines