Building a powerful AI agent requires a structured, strategic approach. While there are extensive guides available on the how of configuring an agent, this post focuses on the what and why — the critical decisions you need to make to ensure your AI agent delivers real business value.

Once you’ve explored these foundational choices, see how they come to life in practice in our companion post, “Inside Lato Bikes’ AI Journey” where we walk through a real-world example of an agent built with Mendix.

1 – Create your agent blueprint

The emergence of AI and agent technology has created a strong impulse for organizations to adopt it simply because it’s new and powerful. However, this adoption often lacks a clear, strategic plan, leading to technology being applied inappropriately or haphazardly, which results in projects that underperform or miss their actual goals.

To avoid this, start with a structured approach. At Mendix, our Digital Execution Practice supports strategic technology adoption through multiple phases – Start, Structure, and Scale – each with distinct milestones and activities that correspond to the 5Ps: People, Portfolio, Process, Platform, and Promotion. Not only does this practice helps organizations align innovation with value, but these same principles also apply when building agentic systems as well.

So, what makes a good first agentic project?

• Value-adding: The agent genuinely improves efficiency, reduces costs, or enhances user experience.
• Synthesis of unstructured data: Agents excel at analyzing text, audio, or video to extract insights.
• Low-risk automation: The agent takes non-critical actions on a user’s behalf.
• Human oversight: Any consequential decisions are reviewed by a person.
• Clear parameters: The agent operates within defined boundaries.

2 – Define success

Before diving into technical specifics, define what success looks like for your AI agent. Without clear, measurable objectives, it’s impossible to gauge effectiveness, justify investment, or guide iterative improvements.

Set Key Performance Indicators (KPIs) that turn your idea into a strategic initiative. Common success metrics include:

• Efficiency gains: How much should the agent reduce manual time?
• Accuracy: How precise should the agent’s classifications or outputs be?
• Speed: How quickly should the overall process improve?

Capturing and tracking these KPIs in a system like Mendix Portfolio Management ensures you’re building toward business impact—not just technical success.

3 – Set the trigger

Before you start building, consider how your agent will be activated. The trigger is the most fundamental decision you’ll make, shaping the entire development process.

There are three primary types of triggers:

• Event-driven: Activated by specific actions such as a new email arriving, an external system making an API call, a manual trigger from a user interface, a change in a database record’s state, or even a business event.
• Batch: Runs at set intervals to process large volumes of data simultaneously. For example, you might run a scheduled event to check all open warranty claims for new status updates.
• Conversational: Triggered by a user’s direct query in a chat interface and the entire experience is a back-and-forth dialogue.

Conversational agents can be incredibly powerful but often represent a more advanced use case. For most organizations, it’s better to start with event-driven or batch workflows to explore agent capabilities safely and effectively.

4 – Design for user experience and deployment

Once you’ve chosen your trigger, consider how users will interact with your agent. Will it live inside an existing app, or will it be a standalone tool?

Ask yourself:

• How does the agent fit into your users’ current workflow?
• What interface makes sense for the experience?
• How will it connect to the systems and data it needs?

With Mendix, agents can be easily embedded into existing apps, such as dashboards or portals, and transported between systems as they evolve. Built-in observability tools ensure administrators can monitor usage and performance in real time.

5 – Choose the right model

Your Large Language Model (LLM) is the foundation of your agent. This decision involves both where the model is hosted and what its capabilities are.

Hosted vs. self-hosted

For most organizations, a hosted LLM like OpenAI (via Azure OpenAI Service), Amazon Bedrock, or Mendix Cloud Gen AI Resource Packs is the most practical choice. These manage infrastructure, scalability, and security for you. Self-hosting open-source models on the other hand offers more control but requires significant infrastructure expertise.

Key considerations

• Tool-use capabilities for real-world actions
• Latency and throughput
• Cost efficiency
• Data security and compliance

A best practice is to take advantage of Agent Commons in Mendix to build model-agnostic agents that can evolve alongside new technologies.

Note on fine-tuning and customization: While base LLMs are powerful, fine-tuning a model on your specific domain data can significantly improve its accuracy and relevance for specialized tasks. This is an advanced technique that should be considered once your initial agent is stable and you have a clear understanding of its performance gaps. It requires dedicated data preparation and computational resources.

6 – Point the agent in the right direction

Once your model is chosen, you need to tell your agent what to do. This is where prompt engineering comes in—the practice of structuring instructions and context so the model behaves as intended.

Agents rely on two types of prompts:

• System prompts: Define the agent’s persona, goals, and constraints.
• User prompts: Trigger the specific task or response.

Clear, specific prompts lead to consistent behavior. Ambiguity leads to unpredictable results. Prompt engineering is iterative—refining prompts over time to optimize performance and reliability.

7 – Equip your agent with tools

What elevates a basic LLM into an agentic system is its ability to take action through tools—functions like Mendix microflows that let it interact with external data, systems, and workflows.

The tools might live locally on your application, or can be connected to via the Model Context Protocol (MCP).

8 – Design for success

Building an agent isn’t a one-and-done project—it’s an iterative process that improves over time. To ensure success, follow these six golden rules:

1. Start simple. Begin with the minimal tools and prompts needed to solve a single problem. Add complexity later.
2. Evolve over time. Test, refine, and version your agents. Mendix Agent Builder makes it easy to track improvements.
3. Be clear and explicit. Anything unsaid will be assumed by the model.
4. Keep a human in the loop. For any decision with real consequences, require a manual review.
5. Manage the context window. Feed your agent only relevant data to avoid confusion.
6. Divide and conquer. Break complex tasks into multiple specialized agents.

Pulling it all together

Successfully integrating AI agents is a strategic journey—one that redefines the way we think about software development. By combining structured design, clear governance, and human oversight, organizations can create agents that truly augment human capability.
Agents offer incredible opportunities to deliver value. The best way to understand them is to start experimenting. Begin your journey with the Mendix Agent Builder Starter App and see what’s possible.