Workflow Assistants
Workflow assistants are an advanced form of AI assistants in AI-School. Instead of an open-ended conversation without a fixed structure, the user follows a defined sequence of steps guided by the AI. This makes it possible to automate complex, multi-step processes — such as a complete lesson from start to finish.
What is a Workflow Assistant?
A workflow assistant is built around a visual workflow of nodes connected by edges. Each node performs a specific task:
- Start / End — the entry and exit point of the workflow
- Evaluate — analyses the context and stores structured information in the workflow memory
- Condition — evaluates an expression and routes the flow in the right direction
- LLM — calls a language model for a specific task (optionally: waits for user input)
- Approval — asks a yes/no question and routes the flow based on the answer
The workflow automatically saves its state after each step. When the user provides input, the workflow resumes exactly where it left off.
Creating a Workflow Assistant
Go to Assistants → New assistant and choose the type Workflow assistant. You can start from a blank canvas or choose one of the pre-configured presets.
For most users, a preset is the best starting point. You can customise the preset to suit your own needs afterwards.
Example: the Lesson Assistant
The Lesson Assistant is a ready-to-use preset designed specifically for education. It guides the user from start to finish through a complete lesson: from determining the topic and level, through learning objectives and explanation, to exercises and a closing summary.
Workflow Overview
The Lesson Assistant consists of the following steps (nodes), in order:
Step by Step
1. Start
The workflow starts as soon as the user opens the chat and sends their first message.
2. Triage: Lesson Topic (evaluate node)
What does it do? The AI analyses the user's message and attempts to determine:
- Topic — what is the lesson about? (e.g. "lever law")
- Level — beginner, intermediate, or advanced?
- Enough info? — is there sufficient context to draft learning objectives directly?
The result is stored in the workflow memory.
3. Enough Info? (condition node)
If enough information is available (enough_info = true), the workflow proceeds directly to Draft learning objectives.
If information is missing, the workflow goes to Gather information.
4. Gather Information (LLM node, waits for input)
What does it do? The AI asks targeted questions to find out the topic and desired level. Once the user has responded, the flow returns to Triage: Lesson Topic to re-evaluate.
Example conversation:
🤖 "Great to have you here! To help you well, a few quick questions: 1. What topic would you like a lesson on? 2. What is your level? (beginner / intermediate / advanced)"
👤 "year 10 physics, lever law"
After this response, the triage re-evaluates and determines: enough_info = true.
5. Draft Learning Objectives (LLM node)
What does it do? The AI drafts a numbered list of up to 4–5 concrete, measurable learning objectives for the given topic and level.
Example output:
Learning objectives for Lever law – year 10 (intermediate):
- You can state the lever law and explain the formula F₁ × r₁ = F₂ × r₂.
- You can perform calculations to determine unknown forces or arm lengths.
- You can apply the lever law to practical examples (scissors, wheelbarrow, seesaw).
- You can explain the difference between a first-, second-, and third-class lever.
6. Approve Learning Objectives (approval node)
What does it do? The AI asks the user whether the drafted objectives are acceptable and whether the lesson can begin.
- Yes → proceed to the lesson
- No → return to Draft learning objectives to reformulate them
The user can view the learning objectives via the expandable menu in the chat.
7. Triage: Learning Objectives (evaluate node)
What does it do? After each lesson cycle, the AI checks whether the user has mastered all learning objectives. This is a strict evaluation: the user must demonstrably understand each objective.
8. All Objectives Understood? (condition node)
- Yes → proceed to Exercises agent
- No → proceed to Lesson agent for the next objective
9. Lesson Agent (LLM node, waits for input)
What does it do? The AI teaches the topic step by step, following the learning objectives. After each part, the AI asks a comprehension question. Only when the user demonstrates understanding does the lesson proceed.
After the user's input, the flow returns to Triage: Learning Objectives to evaluate progress.
Example:
🤖 "We start with objective 1: the lever law. A lever is a rigid rod that rotates around a pivot point. The force you need depends on the distance to the pivot. The formula is:
F₁ × r₁ = F₂ × r₂
Can you explain what r₁ and r₂ mean in this formula?"
10. Exercises Agent (LLM node, waits for input)
What does it do? The AI provides at least one practice question per learning objective. The user answers the questions, after which the flow proceeds to Feedback agent.
11. Feedback Agent (LLM node, waits for input)
What does it do? The AI gives detailed, personal feedback on the completed exercises. For each learning objective, it discusses what went well and where improvement is possible. The tone is honest but encouraging.
12. Wrap Up? (approval node)
The AI asks whether the user wants to wrap up or practise more.
- Yes → go to Summary
- No → return to Exercises agent for another round
13. Summary (LLM node)
What does it do? The AI gives a clear summary of all lesson content, followed by personal, motivating feedback on performance.
14. End
The workflow closes with an encouraging final message.
"Well done during this lesson — see you next time!"
Sample Conversation
A condensed example conversation with the Lesson Assistant on the lever law, year 10:
| Who | Message |
|---|---|
| 👤 User | hi |
| 🤖 Lesson assistant | Great to see you! What topic would you like a lesson on, and at what level? |
| 👤 User | year 10 physics, lever law |
| 🤖 Lesson assistant | Perfect! I've drafted 4 learning objectives. Would you like to start the lesson with these? |
| 👤 User | yes |
| 🤖 Lesson assistant | Let's start with objective 1: the lever law. (explanation + comprehension question) |
| 👤 User | (answer to comprehension question) |
| 🤖 Lesson assistant | (lesson continues for objectives 2, 3, 4 ...) |
| 🤖 Lesson assistant | You've completed all learning objectives! Time for exercises... |
| 👤 User | (answers to practice questions) |
| 🤖 Lesson assistant | (personal feedback) Would you like to wrap up or practise more? |
| 👤 User | wrap up |
| 🤖 Lesson assistant | (summary + motivating closing) |
Restoring a Conversation
If you are in the middle of a lesson and want to go back to an earlier point, you can do so using the restore button (the history icon) on an earlier message. This removes all messages after that point and resumes the workflow from that exact moment.
Restoring is permanent — the deleted messages cannot be recovered.
Editing a Workflow
As an administrator, you can open and edit an existing workflow in the workflow editor. Click a node to change its configuration, add new nodes via the right-click menu, or drag connections to other nodes.
For a completely new workflow, you can start with a blank canvas or use a different preset.