How to build an agent

Think about the work

When designing your AI agent, it's important to think about the work it needs to perform. Start by asking yourself these key questions:

• What specific information does your agent need to accomplish its work?
• What are the step-by-step processes your agent should follow?
• Which systems or applications does your agent need to interact with?

Once you've thought about these questions, you're ready to write your runbook.

Write the runbook

Write clear, detailed instructions for your agent using natural language. Your instructions should outline how the agent should approach and complete its tasks. When writing your runbook:

• Clearly define the agent's overall objective
• Break down the process into logical, sequential steps
• Use structured formatting (e.g., headings, bullet points, numbered lists) to organize information
• Provide context and explanations where necessary

Remember, Large Language Models (LLMs) excel at interpreting text, so a well-organized runbook will significantly improve your agent's performance.

Configuring large language models (LLMs)

Large Language Models (LLMs) help your agents reason, plan, and execute your work. They enable agents to understand and generate human-like text, reason about complex problems, and interpret context. LLMs vendors and versions have different capabilities, strengths, costs, and specializations. Sema4.ai Studio allows you to choose and configure the LLM that best suits your agent's needs.

When configuring LLMs, consider factors such as:

• The complexity of tasks your agent will perform
• The desired balance between performance and cost
• The strengths of the models (e.g., coding, mathematical reasoning, or writing)

By selecting the right LLM and fine-tuning its configuration, you can optimize your agent's language processing capabilities for the type of work it's doing.

Add actions to your agent

To take on real, meaningful work your agent needs actions to connect it to your existing apps and data. Sema4.ai provides three ways to add actions to your agent:

• MCP Servers: Use MCP servers to connect your agent to external systems and services.
• Pre-built Actions: Browse the Action Gallery in Studio to find ready-to-use actions for common applications.
• Custom Actions: If the Action Gallery doesn't have what you need, you can quickly create your own custom actions.

Creating custom actions is simple, but it requires basic Python programming skills. If you're not a developer, collaborate with your development team for this step.

Test your agent

Once you've built your agent, it's helpful to test it in a local environment before sharing it with others. Sema4.ai Studio gives you a complete environment to quckly test and iterate agents locally.

Local testing helps you:

• Check if the agent follows its runbook correctly
• Test your agent with different inputs and scenarios to see how it reacts
• Make sure all actions work properly with your applications

Publish and deploy!

When your agent is working well in Studio, it's time to share it with your organization by publishing it to Control Room and deploying it to your users in Sema4.ai Work Room or through Slack and MS Teams.