> ## Documentation Index
> Fetch the complete documentation index at: https://aletyx.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Decisions

> Author, test, deploy, and govern business decisions with DMN and DRL on Aletyx - deterministic, auditable logic running on Drools and Kogito.

Decisions capture business logic as inputs, rules, and outputs that are
versioned, tested, and governed independently of your application code. Because
they run on a rules engine rather than a probabilistic model, decisions deliver
**completely predictable outcomes** — identical inputs always produce identical
results, and every outcome records exactly which rules fired and why. That
determinism and traceability is what makes decisions auditable for regulated
industries, where black-box models fall short.

Aletyx runs decisions on its enterprise build of **Drools and Kogito**, with
**microsecond-level execution** that can evaluate thousands of rules in parallel —
suitable for high-volume transaction processing and real-time use cases like
fraud detection.

## Authoring approaches

You can express decision logic in three complementary ways. Most teams mix them:
DMN for transparent, business-owned logic and DRL where they need more power.

<CardGroup cols={3}>
  <Card title="DMN" icon="diagram-project">
    Visual, standards-based decision modeling for business users and analysts.
  </Card>

  <Card title="Decision tables" icon="table-cells">
    Spreadsheet-style rule authoring that non-developers can read and change.
  </Card>

  <Card title="DRL" icon="code">
    The native Drools rule language for complex inference beyond tables.
  </Card>
</CardGroup>

## DMN (Decision Model and Notation)

DMN is an OMG industry standard that separates decision logic from process flow.
Aletyx supports DMN at **conformance level 3** — the highest standard — so models
remain portable and vendor-neutral while still executing precisely. See the
[DMN reference](https://docs.aletyx.ai/core/decisions/dmn/).

### Decision Requirements Diagrams (DRDs)

A DRD is the visual map of a decision: **input data**, **decision nodes**, and the
**information requirements** (solid arrows) that show how each decision draws on
inputs and other decisions.

### Decision tables and hit policies

Decision tables lay out conditions and outputs in rows so business analysts can
author and review rules directly. A **hit policy** controls how multiple matching
rows resolve. Aletyx supports all ten DMN hit policies:

| Policy                 | Behavior                                          |
| ---------------------- | ------------------------------------------------- |
| **Unique (U)**         | Exactly one rule may match                        |
| **Any (A)**            | Multiple rules may match if outputs are identical |
| **Priority (P)**       | Highest-priority output wins                      |
| **First (F)**          | First matching rule wins                          |
| **Collect (C)**        | Aggregate all matching outputs                    |
| **C+ / C# / C\< / C>** | Sum / count / min / max variants of Collect       |

### FEEL and boxed expressions

**FEEL** (Friendly Enough Expression Language) is the DMN standard's expression
language — strongly typed, side-effect free, business-friendly, and rich in
built-in functions. Beyond decision tables, DMN offers other **boxed
expressions**: literal expressions, contexts (name-value pairs), relations
(tabular data), functions (reusable logic), invocations (calls to business
knowledge models), and lists. Aletyx also supports custom, domain-specific data
types alongside numbers, strings, dates, and complex structures.

<Tip>
  Start in the [Aletyx Playground](https://playground.aletyx.ai): build a DRD,
  implement the logic, and test it interactively. See the
  [Decision Analyst quickstart](/docs/quickstart/decision-analysts) for a guided walk
  through your first model.
</Tip>

## DRL (Drools Rule Language)

DRL is the native rule language for logic that goes beyond what decision tables
express. Rules follow a declarative **when/then** pattern and run on the Drools
inference engine. See the [DRL reference](https://docs.aletyx.ai/core/decisions/drl/).

* **Conditions (`when` / LHS)** — pattern matching that evaluates *facts* against
  business criteria.
* **Actions (`then` / RHS)** — operations run when conditions match, such as
  modifying working memory or calling functions.
* **Facts & working memory** — facts are the data rules evaluate; the engine
  tracks them in working memory and re-evaluates as they change.
* **Phreak engine** — Drools' high-performance pattern-matching algorithm fires
  rules efficiently as facts change.
* **Rule units & queries** — organize rules into units and query working memory
  for matching data.

**DMN vs. DRL:** reach for DMN when business users own declarative, table-style
logic that must stay transparent and portable; reach for DRL for complex
procedural inference, chaining, and fine-grained control. See
[DMN vs. DRL vs. BPMN](/docs/faq/concepts/dmn-drl-bpmn).

## Testing

Validate decisions before they reach production:

* **Real-time testing** in the Playground as you edit a model.
* **Natural-language testing** — business users validate logic in plain English
  (for example, *"What happens if income is \$85,000 and credit score is 720?"*)
  and get immediate feedback with no technical knowledge required.
* **Test scenarios** that assert expected outputs for representative inputs and
  guard against regressions.

## Governance with Decision Control

[Decision Control](https://docs.aletyx.ai/decision-control/) manages decisions as
governed, testable assets across their full lifecycle:

* **Multi-environment promotion** — isolated Development → Test/UAT → Production
  environments with governance checkpoints and standard, fast-track, or
  self-service approval workflows.
* **Four-eyes principle** — the creator of a change cannot approve their own
  change; separation of duties is enforced.
* **Complete audit trail** — every change records **who** made it, **when**,
  **what** changed, and **why** (a required justification) — regulator-ready
  documentation and the foundation of
  [Accountable AI](/docs/faq/concepts/accountable-ai).

## Safe AI integration

Aletyx applies **deterministic guardrails** so LLM outputs can't override business
policy or produce unaudited decisions. You can also expose DMN models to LLMs
automatically through the **Model Context Protocol (MCP)** — no custom integration
code. See [Decisions + GenAI/MCP](https://docs.aletyx.ai/components/mcp/) and
[Connecting DMN decisions to LLMs with MCP](/docs/faq/products-features/dmn-mcp).

## Deployment & integration

Package decisions as cloud-native services and promote them across environments.
Integration options include:

* **REST APIs** for millisecond decision execution.
* **Event-driven** invocation via Kafka.
* **SSO** through OAuth2/OIDC.
* **Kubernetes-native** deployment; builds distributed via Maven and container
  registries.

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart for Decision Analysts" icon="table-cells" href="/docs/quickstart/decision-analysts">
    Model your first decision visually.
  </Card>

  <Card title="Processes" icon="arrows-spin" href="/docs/guides/processes">
    Orchestrate decisions inside business processes.
  </Card>

  <Card title="Decision Control" icon="shield-check" href="https://docs.aletyx.ai/decision-control/">
    Governance, versioning, and multi-environment deployment.
  </Card>
</CardGroup>