How to ask a decision model

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# How to ask a decision model

This guide shows how to make an mm agent out of a decision model — a model that reads a situation and answers typed questions with a probability per option, without writing text — and how to read its answers from the command line, from a script, from the terminal and from another agent. It assumes you know how to [write an agent file](create-an-agent.md). The examples use LiquidAI d1-3B on llama-server; the finished files in `demos/decision-skills/` use the llmman variant below.

## Steps

### 1. Serve a decision model

Start a server that has the `/v1/systemone` route, with a model it accepts as a decision model:

```bash
llama-server -hf LiquidAI/d1-3B-GGUF:Q4_K_M --port 8090
```

### 2. Write the decider agent

A decider is an agent file with a `decision:` block. Each question has a name, a type and its criteria:

```yaml
# agent.decider.yaml
provider: llamacpp
model: LiquidAI/d1-3B-GGUF:Q4_K_M
baseUrl: http://127.0.0.1:8090/v1

decision:
  questions:
    risk:
      type: choice                 # one option out of the criteria
      instructions: How risky is running this shell command in the project directory?
      criteria:
        read-only: Only reads files or prints information
        local-change: Changes files inside the project, easy to rebuild or revert
        dangerous: Deletes or changes things outside the project, uses sudo, the network, git push or force
    destructive:
      type: noul                   # the probability that the statement is true
      instructions: The command deletes data that cannot be rebuilt.
    urgency:
      type: score                  # a level, lowest first
      instructions: How carefully should a person review this command before it runs?
      criteria: [no review needed, a quick look, a careful review]

# A decider declares no tool: mm refuses to start otherwise.
bashTool: false
editTools: false
planTool: false
fetchContentTool: false
askUserTool: false
skillsDir: no-skills
agentsMd:
  enabled: false
```

The message you send is the state the questions are about. Write the instructions in English: that is what the models seen so far were trained on.

### 3. Ask it from the command line or a script

```bash
mm -config agent.decider.yaml -p "rm -rf ~/.config && git push --force origin main"
```

stdout carries the server's JSON and nothing else, so a script can parse it:

```json
{"model":"LiquidAI/d1-3B-GGUF:Q4_K_M","answers":{"risk":{"type":"choice","choice":"dangerous","probabilities":{"read-only":0.007,"local-change":0.034,"dangerous":0.959},"confidence":0.939},"destructive":{"type":"noul","noul":0.882},"urgency":{"type":"score","score":1.887,"legend":{"0":"no review needed","1":"a quick look","2":"a careful review"},"probabilities":{"0":0.029,"1":0.056,"2":0.916},"confidence":0.830}},"usage":{"input_tokens":172,"output_tokens":0}}
```

The exit status is 0 when the server answered, 1 otherwise (the reason is on stderr). In a [hook](guard-the-agent-with-hooks.md), run this command and read `answers.<name>`, the same way the guard demo reads Granite Guardian's verdicts.

### 4. Ask it from another agent

Declare it in the asking agent's `mmConfigs`:

```yaml
mmConfigs:
  decider: agent.decider.yaml
```

Then `/ask-decider git push --force origin main` in the terminal, the window or an editor, or `mm --ask decider -p "…"` from a skill, shows one line per question followed by the JSON:

```
risk: dangerous (confidence 0.94) — dangerous 0.96 · local-change 0.03 · read-only 0.01
destructive: 0.88
urgency: 1.89 → a careful review (confidence 0.83) — a careful review 0.92 · a quick look 0.06 · no review needed 0.03
```

`mm -config agent.decider.yaml` on its own does the same for every line you type.

## Variants

- **llmman or Ollama**: `provider: llmman` (or `ollama`), `baseUrl: http://127.0.0.1:17434/v1` (or `:11434/v1`), `model: hf.co/LiquidAI/d1-3B-GGUF:Q4_K_M` — pull it first. All three servers gave the same answers for the same request.
- **Another OpenAI-compatible server**: `provider: openai` and its `baseUrl`; mm posts to `/v1/systemone` at the server's root (`baseUrl` without its trailing `/v1`), and a server without the route answers 404.
- **From a sandbox**: add `fallback: http://host.docker.internal:8090/v1`.
- **Without any chat model**: `demos/decision-only/` triages a folder of support messages with one `mm -p` call per message, read by a short Node.js script that prints a table, most urgent first.

## Troubleshooting

| Symptom | Cause | Fix |
|---------|-------|-----|
| `HTTP 501: This model is not a decision model` (llmman, llama-server) or `HTTP 400: model "…" is not a llama.cpp decision model` (Ollama) | the server does not treat the model as a decision model (evalengine's `decision-4b` is refused) | use a model the route accepts, such as LiquidAI d1 |
| `decision: dmr: no /v1/systemone route` at start-up | Docker Model Runner has no such route | serve the model with llama-server, llmman or Ollama |
| `decision cannot be used with tools — this agent declares: …` | a tool is still on | turn off the keys the message lists |
| the first answer takes ~13 s | the server loads the model on the first call | nothing: later calls take 0.2–0.5 s |

## See also

- Every key and every answer field: [Configuration reference](../reference/configuration.md#decision)
- Using a decider to pick the skill before each turn: [How to route skills with a decision model](route-skills-with-a-decision-model.md)
- Why it is built this way: [Decision models](../explanation/decision-models.md)