Instructed Assistant
The Instructed Assistant is the simplest agent blueprint on the platform. It wraps a language model directly: you give it a set of plain-text instructions (a system prompt), and it answers the user's messages accordingly. There is no document retrieval, no knowledge base, and no human escalation — just a configured model that behaves the way your instructions describe.
Think of it as a focused, branded version of a general chatbot. Where a raw model has no personality and no rules, an Instructed Assistant profile can be told who it is, what tone to use, what it should and shouldn't do, and which model to run on — all without writing any code.
When to reach for this agent
Use the Instructed Assistant when the task only needs the model's own reasoning and language skills — drafting, rewriting, summarising pasted text, brainstorming, translation, or answering general questions. If the assistant needs to look things up in your company's documents, use the Document Intelligence Assistant instead.
What it does
The Instructed Assistant runs a short, fixed workflow every time a user sends a message:
- Prepare the conversation. The chat history is trimmed so it fits inside the model's input budget and the thread gets its title. If the message alone is larger than the model's context window, the assistant says so in the chat and stops, rather than failing with an error.
- Gather context. In parallel, it recalls what it remembers about the user and the organization, reads any files the user attached, and searches any knowledge collections the user pointed it at with
#. Each is empty when it does not apply. - Compose. Your system prompt and whatever was gathered are merged into the model's system instructions, within the input budget. The event history shows exactly what the model was given.
- Answer. The response is streamed back token by token, with follow-up suggestions, and new facts about the user are stored in the background.
There is no search of your document collections unless the user asks for one with #, so the assistant stays fast, cheap, and predictable, which makes it a good default for assistants that don't need to consult your data.
What it does not do
It helps to be explicit about the boundaries, because the name "assistant" can suggest more than this blueprint offers:
- No knowledge base of its own. It searches your documents only for collections a user references with
#, and reads files a user attaches. If you ask it about an internal policy without either, it will answer from the model's general training. For grounded, cited answers on a fixed set of collections, use the Document Intelligence Assistant. - No tools or actions. It cannot create tickets, send messages, or call external systems. For that, use the MCP Tool Agent, or the Universal Agent for tools the model chooses.
- No human escalation. It will not hand a question off to a colleague. For that, see the Expert Coordinator Agent.
- No "training" on your data. Like every agent on the platform, it is not fine-tuned on your content. Its behaviour comes entirely from the system prompt and the chosen model. See the Agents overview for why the platform uses configuration and retrieval rather than training.
Typical scenarios
- A writing helper. "You are a helpful assistant that rewrites text to be clear and concise. Always keep the original meaning." Employees paste a paragraph and get a cleaned-up version.
- A tone-of-voice assistant. A marketing team configures the system prompt with their brand voice and style rules, giving everyone a consistent drafting helper.
- A translation assistant. "Translate everything the user sends into formal German. Output only the translation."
- A constrained Q&A bot. A general-knowledge helper that is instructed to stay on a specific topic and politely decline anything else.
Setting it up
Like every agent, the Instructed Assistant is delivered as a blueprint (the read-only template) from which you create one or more profiles (your configured, named instances). If you are new to that distinction, read Blueprints & Profiles first. To create a working assistant:
- Open the blueprint. Go to Admin > Agents > Blueprints and select Instructed Assistant. If it shows as offline, you can still configure profiles — they activate once the agent service is running.
- Create a profile. Click Create Profile and give it an Agent ID (a short, URL-safe slug such as
writing-helper), a Name, a Description, and an Icon. These are what users see when they pick an assistant to talk to. - Write the system prompt. This is the heart of the assistant. Describe its role, its tone, what it should do, and what it must refuse. Be concrete — see the best practices below.
- Choose the model. Pick a chat model from the dropdown. The available options come from your platform's LiteLLM configuration. Match the model to the task: a smaller, cheaper model is fine for rewriting and translation; a larger one helps for nuanced reasoning.
- Tune the parameters (optional). Adjust the model parameters, the input-token budget, and the timeout if the defaults don't suit your use case. The defaults are sensible for most assistants.
- Save. The profile is available immediately to any user with permission to use it.
There is nothing else to set up
Unlike the document- or expert-oriented agents, the Instructed Assistant has no external dependencies. You do not need to create a knowledge base, run a data pipeline, or connect a chat channel. A model and a system prompt are all it needs.
Configuration reference
The configuration form is grouped into three parts: the profile identity (shared by every agent), the assistant's behaviour, and the language-model settings.
Profile identity
These fields exist on every agent blueprint and define how the profile appears in the UI.
| Field | Type | Required | Description |
|---|---|---|---|
| Agent ID | Text | Yes | Unique, URL-safe identifier for this profile. Lowercase letters, digits, underscores, hyphens only. |
| Name | Text (per language) | Yes | Display name shown to users. Can be set per language (de, en, fr, it). |
| Description | Text (per language) | Yes | Short explanation of what this profile is for. Shown in the assistant picker. |
| Icon | Icon picker | No | Visual identifier. Defaults to a generic robot icon. |
Behaviour
These fields control how the assistant treats each conversation.
| Field | Type | Default | Description |
|---|---|---|---|
| System Prompt | Long text | (empty) | The plain-text instructions that define the assistant's role, tone, and rules. Inserted at the start of every conversation. This is the single most important setting. |
| Maximum Input Tokens | Number (optional) | Model window (85%) | The size of the input budget. The conversation (system prompt + chat history + new message) is trimmed to fit. Lower values cut cost and keep the model focused on recent turns; higher values preserve more history. Optional. Unticked by default: the agent fills about 85% of the selected chat model's input window, which is shown in the checkbox label and the disabled field (e.g. "(78,050)" for gemma on Infomaniak) and follows the model when you switch models. The 15% margin allows for differences between the token counter and the model's tokenizer. Tick Set your own input limit to set a lower limit and cut cost: minimum 1,024, maximum that same 85% figure, which the hint under the field names next to the model's full window; a higher value is blocked in the form and rejected by the API. Profiles that stored a limit above that figure have it unset automatically when the platform is upgraded. |
The input budget follows your model
Left unticked, Maximum Input Tokens fills 85% of the chosen model's input window and follows it when you switch models. A limit of your own is capped at that same figure, shown under the field. The assistant also checks the message against the model's real window and answers with a short notice when it cannot fit.
The form also contains the shared chat sections (memory, attached files, referenced knowledge, task model). See Shared chat settings.
Language model
These settings live under the model section of the form. They select the model and control how it generates text.
| Field | Type | Default | Description |
|---|---|---|---|
| Model | Model picker | — | Which chat model the assistant runs on. Options come from your LiteLLM configuration. Required. |
| Temperature | Number (optional) | Model default | Controls randomness. Unticked by default: the model's own recommended default is used and shown in the checkbox label and the disabled field. Tick Override the model's default temperature to set 0.0–2.0; 0.0 gives focused, repeatable answers (translation, extraction, factual replies), higher values more varied output. Helper steps (checks, titles, question condensation) use 0.0 while it is unticked; a value you set applies to them too. |
| Breadth of word choice (top_p) | Number (optional) | Model default | How many alternatives the model considers for each next word (0–1), with the same override checkbox. |
| Accept images | Checkbox | On | Whether images in the conversation are passed to the model. Disabled for models without image support. |
| Return Log Probabilities | Toggle | Off | Whether the model returns token-level probabilities. An advanced/diagnostic option; leave off unless you specifically need confidence scores. |
| Top Log Probabilities | Number | 0 | How many of the most likely alternative tokens to return per position. Only applies when log probabilities are enabled. Range: 0–20. |
| Timeout | Number (seconds) | 300 | How long to wait for the model before giving up. Increase only if you use a slow model with very long outputs. |
How the settings combine at runtime
When a message arrives, the assistant builds the conversation as [ system messages → your system prompt → chat history → new user message ], trims it to Maximum Input Tokens, then calls the selected Model with the configured model parameters (Temperature, Breadth of word choice (top_p), Timeout, log-probability options). The reply streams straight back to the chat UI.
Best practices
Write the system prompt like a job description. State the role ("You are an IT support assistant for internal staff"), the behaviour ("Answer in short, numbered steps"), and the limits ("If a question is not about IT, say you can only help with IT topics"). Vague prompts produce vague assistants.
Start with a low temperature. 0.0–0.3 is right for most business assistants — it makes answers consistent and predictable. Switch the temperature override on and set it, since the default now follows the model. Only raise it for genuinely creative tasks like brainstorming or copywriting.
Keep one profile per purpose. Rather than one "does everything" assistant, create focused profiles ("Email Drafter", "German Translator", "IT FAQ Bot"). Each gets a tighter system prompt and is easier for users to choose between.
Pick the smallest model that does the job. Rewriting, translating, and summarising rarely need your largest model. Reserve the expensive models for profiles that genuinely need stronger reasoning.
Remember it can't see your documents. If users keep asking it about internal policies or files, that's a signal you need the Document Intelligence Assistant instead.