Lab 10Personalize an agent for its users

A company builds digital assistants for fitness centres. Its Gym Agent greets the user, waits for a question, sorts it into training, nutrition or other, gives a generic answer and goes back to waiting. That works for an average visitor, but not for everyone: an elderly visitor may prefer spoken interaction and larger text, and a visitor who uses a wheelchair needs workouts that do not rely on the legs.

In this lab you model those two users and let BESSER adapt the agent to each. You describe each profile in a User diagram, attach a customization to it on the Agent Customization page, and generate a single agent that asks the user for their profile and then runs the matching variant. The editor part needs only a browser. Running the agent is optional and needs Python, Docker and an OpenAI key.

The lab assumes you know the editor basics from Your first class diagram. Build agents with the BESSER Agentic Framework explains how the generated agent code works, but you do not need it here.

Load the Gym Agent and inspect it

1. Open editor.besser-pearl.org, choose Start modelling, name the project Gym Coach, pick the Agent Developer perspective and click Create Project. The sidebar now shows Agent and User.
2. Click Agent, then open File > Load Template, select the Agent Diagram category, select Gym Agent and click Load Template.
3. Zoom out with the - button at the bottom right until you see the whole diagram.

Read the diagram:

• States are the rounded boxes. Each row under the name is one reply the agent sends when it enters the state. Idle has a second, fallback reply for messages that match no intent. OtherQuestions has a single AI response row: an LLM answers.
• Transitions are the arrows. Auto fires as soon as the state has replied. Muscles_intent, Nutrition_intent and Other fire when the user’s message matches that intent.

4. Click Components under Agent and open Intents. Expand Muscles_intent.

Give the agent an LLM

The template uses an LLM in two places: to classify messages into intents and to answer in OtherQuestions. The personalized variants also pass their profile to this LLM. Declare it now, before you create variants.

1. On Components, open LLMs and click Add LLM. Enter gpt-4o-mini as Model Name, keep Provider on OpenAI and tick Set as default LLM.

   

2. Click Agent Customization under Agent. On the Agent Runtime tab, set Intent Recognition to LLM-based. Keep Platform on WebSocket with Use Streamlit UI ticked.

   

Model the Elderly profile

A User diagram describes a group of users by criteria on a fixed user model: User is the root, with parts such as Personal_Information, Accessibility, Competence and Culture. You can drag these parts from the palette and link them, but the form editor is faster and cannot produce an invalid structure.

1. Click User in the sidebar. The palette on the left lists User, Personal_Information, Competence, Accessibility, Culture, Language and more.

2. Click Edit as Form at the top right. The User Profile Form opens with User as ROOT.

3. Tick Include next to Personal Information, then click Personal Information to expand it.

4. In the age row, choose > in the operator list and enter 65. Leave the other fields empty: an empty field is not a criterion.

   

5. Close the form with the cross at its top right.

6. Double-click the diagram tab User Diagram, type Elderly and press Enter.

7. Click Quality Check in the top bar.

Model the Paraplegic profile

1. Click + next to the Elderly tab to add a second User diagram.

2. Click Edit as Form, tick Include next to Accessibility and expand it.

3. Next to Disability, click Add, then click Disability to expand it.

4. Fill in Disability 1: name = Paraplegic, description = Cannot use lower body, and choose Mobility for affects.

   

5. Close the form, rename the tab to Paraplegic and click Quality Check.

Customize the agent for the Elderly profile

1. Click Agent, then Agent Customization, and switch to the Personalization tab.

2. Under User Profile Mapping, choose Elderly.

   

   The three Automatically propose configuration using … buttons fill the form for you, from predefined rules, an LLM or a RAG source. They need a GitHub sign-in (GitHub button in the top bar); without one they show Sign in to GitHub to use recommendations. You can skip them and set the values by hand.

3. Under Personalization Overview, click Presentation. Under Style of text in interface, set Size to 20 and Contrast to High.

   

4. Click Modality and tick Enable speech input and Enable speech output.

   

5. Scroll to Save this customization, enter Elderly as Customization Name and click Save & Apply Configuration.

   

Customize the agent for the Paraplegic profile

1. Open Agent Customization > Personalization again. Under User Profile Mapping, choose Paraplegic.

2. Click Content and tick Adapt content to user profile.

   

3. Enter Paraplegic as Customization Name and click Save & Apply Configuration.

4. Use the variant selector in the top bar to switch between Base agent model, Elderly (Elderly) and Paraplegic (Paraplegic). Each entry is a separate copy of the agent diagram; the base model stays untouched.

Generate the personalized agent

1. Select Base agent model in the variant selector, so the agent diagram is active.

2. Open Generate > BESSER Agent.

3. In the Select Agent Languages dialog, under Personalization Strategy, choose Personalization (all) and click Generate.

   

4. Unzip the downloaded agent_output.zip.

Run the agent with its user database

The personalized agent asks every user to log in and to pick a profile, and stores users and sessions in PostgreSQL. You run that database in Docker.

1. Install BAF if you have not yet, in a virtual environment: pip install "besser-agentic-framework[extras,llms]" (see Build agents with the BESSER Agentic Framework).

2. In a separate folder, download the Dockerfile and start the database:

   docker build -t agentdb .
   docker run -d --name agentdb -p 5432:5432 agentdb

3. Download config.yaml and use it to replace the generated config.yaml in the unzipped agent folder. It enables the monitoring and streamlit databases with the credentials of the Dockerfile. Replace YOUR-API-KEY with your OpenAI key.

4. From the agent folder, run:

   python Gym_Agent.py

5. Open http://localhost:5000. Enter any username and password and click Login: an unknown username creates a new account.

6. Click Choose Your User Profile, flip to the profile you want with Next >, click Confirm as fitting to me, then click Chat with Agent.

Exercise: add a third user group

Show a solution

Add a third User diagram tab with Personal Information included and age < 18, then save a customization mapped to it. For comparison, File > Load Template > Full Project > Personalized Gym Agent opens a ready-made project (as a new project) with a Teenager and a ParaplegicUser profile.

Show a solution

Edit the Base agent model: add the intent on Components > Intents, then the state and its transitions on the canvas. A variant is a snapshot taken at Save & Apply Configuration time, and the editor re-personalizes from its stored copy of the base model, which it refreshes when you switch variants. So switch to a variant and back to Base agent model, load each saved customization with Load a saved customization, apply it again, and regenerate.