Build AI agents for Business Central

What are AI agents?

An AI agent is, in short, a software program that can assist you by independently understanding a task, finding relevant information, and taking actions—almost like a digital assistant. These agents use technologies such as machine learning and generative AI to perform tasks autonomously, make decisions, and automate processes.

Imagine, for example, an employee who can simultaneously listen, think, and act. The agent receives an input—such as a question—processes it using AI, and then takes action, such as retrieving data, providing an answer, or updating a system like Microsoft Dynamics 365 Business Central.

AI agents can, for example:

• Answer questions in natural language and assist users via chat
• Automate a wide range of work tasks
• Analyze your business data and provide recommendations for actions
• Independently perform actions within a business solution like Business Central

In short, AI agents function as intelligent digital employees that can work quickly, consistently, and around the clock.

Why build agents for Business Central?

As they operate on top of your existing data and processes, building AI agents for Business Central can significantly increase your direct business value:

• Automate manual work: By letting agents handle repetitive tasks such as bookkeeping, order creation, and follow-ups, you can save both time and resources.
• Get faster answers and insights: Ask your agents questions in natural language and receive instant answers based on your own business data.
• Make better decisions: Your agents can quickly analyze patterns in your business data and provide real-time action recommendations.
• Increase productivity: With the help of agents, your employees can spend less time on administration and focus more on value-creating work..
• Work more proactively: Your agents can monitor your business operations and alert you to deviations, issues, or opportunities.

In short, AI agents make Business Central more efficient, intelligent, and action-oriented in everyday operations.

How do you build agents in Business Central?

You build AI agents for Business Central by using Microsoft Copilot Studio to define their purpose, conversations, and actions. You then integrate them with Microsoft Dynamics 365 Business Central via connectors or APIs so they can retrieve data and perform tasks automatically.

1. Define the purpose: Start by clarifying what the agent should help with—for example bookkeeping, inventory lookup, or customer service. The more specific the use case, the better the results.
2. Create an agent in Copilot Studio: Open Copilot Studio and create a new agent. Choose an appropriate language and give it a clear description of its role.
3. Connect to Business Central: Use standard connectors or APIs to integrate with Business Central. This allows the agent to retrieve and update data (e.g., customers, orders, and finance).
4. Build dialogs and actions: Set up topics and flows that define how the agent responds and acts. Use Power Platform flows to execute concrete actions in Business Central.
5. Add AI and prompts: Use generative AI to make the agent more flexible. Define prompts so it can understand and respond to complex requests.
6. Test and refine: Test the agent with realistic scenarios. Fix errors, improve responses, and continuously optimize flows..
7. Deploy and monitor: Once ready, deploy the agent in Teams, web, or other channels. Monitor usage and performance to ensure ongoing improvements.

When should you not use AI agents in Business Central?

You should not use AI agents in Business Central when tasks are very simple, standardized, and follow fixed rules. In such cases, traditional workflows or automations are often more stable, cost-effective, and easier to maintain.

It is also not ideal to use AI agents if the data foundation is insufficient or of low quality, as the agent’s output largely depends on the data it works with. Unclear or poorly defined processes can likewise lead to inaccurate results.

Additionally, caution should be exercised in situations with high requirements for precision, control, and compliance—such as financial or legal processes—where even small errors can have significant consequences. In these cases, AI can still be used as a support tool, but not as the primary decision-maker.