# Aletyx

> Enterprise decision and process automation platform.

## Docs

- [Overview](https://aletyx.ai/docs/ai-assistant/overview.md): The Aletyx AI Assistant helps you build decision models in natural language - describe what you want and it builds, changes, and explains models for you.
- [Prompts](https://aletyx.ai/docs/ai-assistant/prompts.md): Prompt ideas to try with the Aletyx AI Assistant on the Loan Pre-Qualification sample model.
- [Setup](https://aletyx.ai/docs/ai-assistant/setup.md): Subscribe to the Aletyx AI Assistant, create your API key, and connect it to the Playground - step by step.
- [Testing](https://aletyx.ai/docs/ai-assistant/testing.md): Use the Aletyx AI Assistant to generate test cases for your decision models - positive and negative, in plain language.
- [Concepts](https://aletyx.ai/docs/architecture/decisions/dmn/concepts.md): Core DMN concepts in Aletyx: DRD components, boxed expressions, decision tables, hit policies, FEEL data types, and decision execution flow.
- [Decision Services](https://aletyx.ai/docs/architecture/decisions/dmn/decision-services.md): Design, invoke, and test DMN decision services in Kogito and Drools - output and encapsulated decisions, boxed invocations, BPMN business rule tasks, and Java API calls.
- [FEEL Handbook](https://aletyx.ai/docs/architecture/decisions/dmn/feel-handbook.md): Comprehensive FEEL reference for DMN in Aletyx - data types, if/for/some/every expressions, three-valued logic, and string, list, context, and date functions.
- [Listeners](https://aletyx.ai/docs/architecture/decisions/dmn/listeners.md): Implement the DMNRuntimeEventListener interface in Kogito and Quarkus to audit, monitor, and validate DMN decision, decision table, and context entry evaluation events.
- [DMN](https://aletyx.ai/docs/architecture/decisions/dmn/overview.md): Overview of DMN in the Aletyx Enterprise Build of Kogito and Drools - DRDs, FEEL, decision tables, hit policies, and conformance level 3 support for DMN 1.1 to 1.5.
- [Decision Automation](https://aletyx.ai/docs/architecture/decisions/overview.md): Overview of decision automation with Drools - the open-source BRMS - covering DRL, rule units, DMN, FEEL, decision tables, and OOPath for declarative business rules.
- [Rule Language](https://aletyx.ai/docs/architecture/decisions/rule-language.md): Structure of a Drools DRL rule - declaration, when conditions (LHS), and then actions (RHS) - with bound variables and worked discount rule examples.
- [Decision & Process](https://aletyx.ai/docs/architecture/integration/decision-process.md): Orchestrate decisions across Kogito rule units, Drools KJAR ruleflows, and BPMN business rule tasks - with sequential, parallel, and conditional patterns over REST and Kafka.
- [Event-Driven](https://aletyx.ai/docs/architecture/integration/event-driven.md): Build event-driven rules, DMN decisions, and jBPM processes in Kogito using native Kafka and CloudEvents integration, message and catch events, and saga choreography.
- [Integration Patterns](https://aletyx.ai/docs/architecture/integration/overview.md): Integration patterns connecting Kogito decisions and processes to enterprise systems - decision-process embedding, event-driven Kafka and CloudEvents, and service orchestration.
- [Service Orchestration](https://aletyx.ai/docs/architecture/integration/service-orchestration.md): Orchestrate multi-service workflows with BPMN Service Tasks - REST and Java calls, async execution, sequential, parallel, conditional routing, error handling, and saga compensation.
- [Architecture](https://aletyx.ai/docs/architecture/overview.md): Architecture of Aletyx Enterprise Build of Kogito and Drools - the DRL, DMN, and decision-table decision engine, BPMN process engine, and rule-process integration patterns.
- [Event-Driven Architecture](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/event-driven.md): Build event-driven rules, DMN decisions, and BPMN processes with Kogito on Quarkus, using out-of-the-box Apache Kafka and CloudEvents integration for event routing.
- [Flexible Processes](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/flexible-processes.md): Model adaptable BPMN workflows in Kogito and Drools using ad-hoc processes, ad-hoc sub-processes, milestones, and ad-hoc fragments for dynamic, unstructured work.
- [GraphQL Integration](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/graphql.md): Query Kogito process instances and user tasks with GraphQL - where filters and operators, and/or logic, orderBy sorting, and limit/offset pagination.
- [Human Tasks](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/human-tasks.md): Manage BPMN human tasks in Kogito and jBPM - task lifecycle and states, user and group assignment, data mapping, forms, deadlines, notifications, and TaskService REST and Java APIs.
- [ISO-8601 Time Formats](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/iso-8601.md): How ISO-8601 dates, durations, and repeating intervals drive BPMN timer events - timeDate, timeDuration, and timeCycle - in jBPM, Drools, and Kogito.
- [Process Listeners](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/listeners.md): Process and task listeners in Aletyx Enterprise Build of Kogito and Drools.
- [Monitoring](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/monitoring.md): Monitor Kogito business processes with the Prometheus add-on and Grafana dashboards, tracking instance counts, SLA violations, and average process execution time.
- [Advanced BPMN](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/overview.md): Advanced Kogito BPMN topics - event-driven extensions, subprocesses and flexible processes, monitoring and custom listeners, GraphQL queries, and Kubernetes deployment.
- [Process Versioning](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/process-versioning.md): Manage BPMN process versioning in Kogito with the parallel deployment pattern - versioned process IDs, client routing via gateways or facades, and cleanup of completed versions.
- [Embedded](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/subprocesses/embedded.md): Embedded (inline) BPMN subprocesses group related tasks within a parent process, share its variables, and support boundary events like timers across the whole group.
- [Event](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/subprocesses/event.md): BPMN event subprocesses in Kogito - triggered by messages, timers, or errors to handle exceptions and compensation, as in SAGA-pattern order workflows.
- [Subprocesses](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/subprocesses/overview.md): Overview of BPMN subprocess types supported in Kogito - embedded, reusable, ad-hoc, and event subprocesses - for modularizing complex business workflows.
- [Reusable](https://aletyx.ai/docs/architecture/processes/advanced-bpmn/subprocesses/reusable.md): Reusable subprocesses in BPMN are standalone, parameterized processes invoked by multiple parent processes to share common logic and reduce redundancy.
- [Adaptive Process Architecture](https://aletyx.ai/docs/architecture/processes/architecture/adaptive-process-architecture.md): Adaptive Process Architecture co-locates jBPM and Kogito services on Quarkus with shared persistence, Data-Index, Jobs Service, and User Tasks for stateful cloud-native process orchestration.
- [Data Intelligence Layer](https://aletyx.ai/docs/architecture/processes/architecture/data-intelligence-layer.md): How the Data Intelligence Layer, built on the Data-Index subsystem, maintains a continuously updated snapshot of process state for real-time visibility into running processes.
- [Engine Architecture](https://aletyx.ai/docs/architecture/processes/architecture/engine.md): How the Apache KIE process engine works - the Kogito state machine powering jBPM (BPMN) and SonataFlow, with pluggable persistence (PostgreSQL, MongoDB, Infinispan, RocksDB).
- [Process History](https://aletyx.ai/docs/architecture/processes/architecture/process-history.md): How the Process History Service, built on the Data-Audit subsystem, provides a comprehensive temporal view of process execution across the lifecycle of process instances.
- [Runtimes](https://aletyx.ai/docs/architecture/processes/architecture/runtimes.md): Execution environments for Apache KIE components: Quarkus as the preferred runtime, Spring Boot as a supported alternative, and embedded Java mode for Drools.
- [Activities](https://aletyx.ai/docs/architecture/processes/basic-bpmn/activities.md): BPMN activities in Kogito - Business Rule, Script, User, Service, and Milestone tasks - with their key features, properties, and when to use each.
- [Process API](https://aletyx.ai/docs/architecture/processes/basic-bpmn/api.md): Interact with Kogito BPMN processes programmatically through the auto-generated domain-specific REST API, the technical /management REST API, and the Java ProcessService API.
- [BPMN Editor](https://aletyx.ai/docs/architecture/processes/basic-bpmn/editor.md): Configure BPMN processes in the Aletyx editor - process properties and variables, script, service, and business rule tasks for DMN and DRL, and XOR gateways.
- [End Events](https://aletyx.ai/docs/architecture/processes/basic-bpmn/end-events.md): BPMN end events in Aletyx Kogito and jBPM - None, Message, Error, and Terminate end nodes that conclude a process flow and optionally throw a message, error, or signal.
- [Gateways](https://aletyx.ai/docs/architecture/processes/basic-bpmn/gateways.md): How BPMN gateways diverge and converge process paths in jBPM - Exclusive (XOR), Inclusive (OR), Parallel (AND), and Event-based gateways for branching logic.
- [Intermediate Events](https://aletyx.ai/docs/architecture/processes/basic-bpmn/intermediate-events.md): BPMN intermediate throwing and catching events in Kogito - timer, message, signal, and compensation events for coordinating interactions within and across processes.
- [BPMN Basics](https://aletyx.ai/docs/architecture/processes/basic-bpmn/overview.md): An introduction to BPMN 2.0 basics in jBPM - flow objects (events, activities, gateways), sequence flows, and how jBPM extends the OMG notation for executable workflows.
- [Start Events](https://aletyx.ai/docs/architecture/processes/basic-bpmn/start-events.md): BPMN start events trigger a process in Kogito and jBPM - choose simple, message, timer, or signal start nodes to match how and when a workflow begins.
- [Data-Index & Data-Audit](https://aletyx.ai/docs/architecture/processes/components/data-index-audit.md): Kogito Data-Index and Data-Audit components - GraphQL querying of process state, audited event storage for compliance, plus deployment and retention options.
- [Job Service](https://aletyx.ai/docs/architecture/processes/components/job-service.md): Schedule and monitor timers, human tasks, and time-based events with Kogito Job Service - independent vs compact deployment, leader-based HA, CDC, and the Temporal Intelligence Coordinator.
- [User Tasks](https://aletyx.ai/docs/architecture/processes/components/user-tasks.md): How BPMN User Task nodes drive human-centric workflows in Apache KIE via the User Task Subsystem - assignment, delegation, lifecycle transitions, and deadlines.
- [Process Automation](https://aletyx.ai/docs/architecture/processes/overview.md): Business automation in the Aletyx Enterprise Build of Kogito and Drools - integrating BPMN workflows, decision management, and task orchestration.
- [Adaptive Process Architecture](https://aletyx.ai/docs/components/compact-architecture/overview.md): Aletyx Adaptive Process Architecture co-locates jBPM process components - Data-Index, Data-Audit, Jobs Service, User Tasks, and storage - in a single Quarkus deployment.
- [Scaling & Performance](https://aletyx.ai/docs/components/compact-architecture/scaling.md): Scale Kogito and Drools process workloads vertically and horizontally with the Temporal Event Coordinator, enabling Kubernetes autoscaling and distributed timer consistency.
- [Securing Intelligent Process Orchestrations - API Endpoints](https://aletyx.ai/docs/components/compact-architecture/securing-the-architecture.md): Secure your Intelligent Process Orchestrations API endpoints with authentication and authorization, with the example focused on Quarkus.
- [jBPM](https://aletyx.ai/docs/components/jbpm.md): jBPM, the Apache KIE Business Process Management engine in the Aletyx build - BPMN 2.0 process execution, runtime and task services, and integration with Drools, Kogito, and Quarkus.
- [Kogito](https://aletyx.ai/docs/components/kogito.md): Kogito is a cloud-native business automation platform for BPMN and DMN on Quarkus or Spring Boot, with native compilation, generated REST APIs, and Kubernetes-native deployment.
- [Model Context Protocol](https://aletyx.ai/docs/components/mcp/overview.md): Model Context Protocol governs how Drools, jBPM, and Kogito automation interacts with LLMs like Claude - a rule-governed AI gateway, context provider, prompt management, and response validation.
- [Platform](https://aletyx.ai/docs/components/overview.md): Overview of the Aletyx platform components built on Apache KIE - the enterprise builds of Drools, jBPM, and Kogito plus the Aletyx Playground, and how they work together.
- [Accelerators](https://aletyx.ai/docs/components/playground/accelerators.md): This guide provides a comprehensive walkthrough for creating, customizing, and deploying project accelerators for the Aletyx Playground.
- [Advanced Accelerators](https://aletyx.ai/docs/components/playground/advanced-accelerator.md): Create and customize Git-based Accelerators for Aletyx Playground - configure profiles, pom.xml, application.properties, Dockerfiles, CI/CD, and Kafka or Kubernetes templates.
- [Executable Processes Models: BPMN in Aletyx Playground](https://aletyx.ai/docs/components/playground/bpmn-tutorial.md): A hands-on walkthrough of exploring, modeling, testing, and deploying BPMN process models in the browser-based Aletyx Playground.
- [Components](https://aletyx.ai/docs/components/playground/components.md): Architecture of the browser-based Aletyx Playground - the Studio Environment, CORS Proxy, Extended Services, and Dev Deployment service for authoring DMN and BPMN models.
- [Customization](https://aletyx.ai/docs/components/playground/customizing.md): Customize Aletyx Playground (KIE Sandbox) - logo and favicon branding, Git provider integration, accelerators, custom commit validation, and editors via Docker and environment variables.
- [Dev Deployments](https://aletyx.ai/docs/components/playground/dev-deployment.md): Quickly test DMN and BPMN assets in containers with Aletyx Playground dev deployments, using standard or custom images - a development-only tool with key limitations, not for production.
- [Executable Rules Models: DMN in Aletyx Playground](https://aletyx.ai/docs/components/playground/dmn-tutorial.md): A 30-minute hands-on walkthrough of building and testing DMN decision models in the browser-based Aletyx Playground.
- [Playground](https://aletyx.ai/docs/components/playground/overview.md): Aletyx Playground, built on Apache KIE Sandbox, is a browser-based tool to model, test, and deploy DMN and BPMN with Git integration, dev deployments, and accelerators.
- [Playground Deployment](https://aletyx.ai/docs/components/playground/self-hosting.md): Configure a self-hosted Aletyx Playground: environment variables, Git providers (GitHub, Bitbucket), CORS proxy and extended services, custom commit validation, branding, and accelerators.
- [Core Concepts](https://aletyx.ai/docs/core-concepts.md): Core concepts of the Aletyx Enterprise Build of Kogito and Drools - how decision and process automation work, for architects and senior developers.
- [Architecture](https://aletyx.ai/docs/decision-control-tower-sidecar/architecture.md): How the Decision Control Tower Sidecar is structured — the workflow engine, process and task state, callback client, and optional integrations.
- [Decision Control Tower Sidecar — Configuration](https://aletyx.ai/docs/decision-control-tower-sidecar/deployment/configuration.md): Environment-variable reference for the Decision Control Tower Sidecar — database, callbacks, and optional integrations.
- [Decision Control Tower Sidecar — Deployment](https://aletyx.ai/docs/decision-control-tower-sidecar/deployment/overview.md): Run the Decision Control Tower Sidecar alongside Tower, with its own PostgreSQL database.
- [Customize the Promotion Workflow](https://aletyx.ai/docs/decision-control-tower-sidecar/how-to/customize-workflow.md): Adapt the Sidecar's example approval workflows to your own policy — what lives in the customizable layer, what is off-limits, and how to make changes safely.
- [Git Model Versioning](https://aletyx.ai/docs/decision-control-tower-sidecar/how-to/git-model-versioning.md): Commit the promoted model's files to a Git repository on each approved promotion — a diffable history of governed models alongside the Tower audit trail.
- [How-To Guides](https://aletyx.ai/docs/decision-control-tower-sidecar/how-to/overview.md): Guides for tailoring the Aletyx Decision Control Tower Sidecar starter — customize its workflows and actions while the Tower contract stays fixed.
- [Aletyx Decision Control Tower Sidecar](https://aletyx.ai/docs/decision-control-tower-sidecar/overview.md): A starter project for the approval workflows behind a promotion — and the workflow engine Aletyx Decision Control Tower depends on at runtime.
- [Aletyx Decision Control Tower Architecture](https://aletyx.ai/docs/decision-control-tower/architecture.md): The Tower's components, how they communicate, and how it is deployed.
- [Portal Tour](https://aletyx.ai/docs/decision-control-tower/dashboard.md): A walkthrough of the Aletyx Decision Control Tower portal: the Home landing, sidebar workspaces, masthead controls, session model, and notification surfaces.
- [Container Configuration](https://aletyx.ai/docs/decision-control-tower/deployment/container-configuration.md): Runtime requirements, environment variables, secrets, and example run commands for the Tower container.
- [Database Configuration](https://aletyx.ai/docs/decision-control-tower/deployment/database-configuration.md): PostgreSQL provisioning, Flyway migrations, and connection settings for Decision Control Tower.
- [Identity & Access Management (OIDC)](https://aletyx.ai/docs/decision-control-tower/deployment/identity-access-management.md): Configure Microsoft Entra ID or Keycloak OIDC authentication for Decision Control Tower.
- [Kubernetes & OpenShift Deployment](https://aletyx.ai/docs/decision-control-tower/deployment/kubernetes-openshift-deployment.md): Reference manifests, sidecar wiring, and OpenShift specifics for deploying Decision Control Tower.
- [Decision Control Tower — Deployment](https://aletyx.ai/docs/decision-control-tower/deployment/overview.md): Quick-start to deploy Aletyx Decision Control Tower (with its required Sidecar) locally or on Kubernetes.
- [Supported Operating Environments](https://aletyx.ai/docs/decision-control-tower/deployment/supported-operating-environments.md): Compatibility matrix for runtimes, orchestrators, databases, identity providers, and browsers.
- [Environment Management](https://aletyx.ai/docs/decision-control-tower/environments.md): Centralized access to all Decision Control environments with environment-specific configuration and governance.
- [Promotion History](https://aletyx.ai/docs/decision-control-tower/how-to/audit-trail.md): Every promotion keeps a permanent record of who requested what, who approved it, when each step happened, and the final outcome.
- [Environments](https://aletyx.ai/docs/decision-control-tower/how-to/environments.md): Browse, filter, tag, and launch the Decision Control environments you have access to from the Environments page.
- [Governance Tasks](https://aletyx.ai/docs/decision-control-tower/how-to/governance-tasks.md): The Tasks inbox is where promotion requests wait for human action - claim, approve, reject, request changes, resubmit, cancel, and annotate tasks.
- [How-To Guides](https://aletyx.ai/docs/decision-control-tower/how-to/overview.md): Task-focused guides for Aletyx Decision Control Tower, the governance portal — each guide answers a single "how do I…?" question with the shortest path to done.
- [Promotions](https://aletyx.ai/docs/decision-control-tower/how-to/promotions.md): Create, track, open, and cancel promotion requests that move a model version from one environment to another through a governed workflow.
- [Sign in & Global](https://aletyx.ai/docs/decision-control-tower/how-to/sign-in-and-global.md): How to get into the Control Tower and use the controls that are available from every screen - the Home page, the top masthead, and the collapsible sidebar.
- [Aletyx Decision Control Tower](https://aletyx.ai/docs/decision-control-tower/overview.md): Unified portal providing centralized access to all Decision Control environments, governance workflows, and audit capabilities.
- [Task Management](https://aletyx.ai/docs/decision-control-tower/tasks.md): Tasks are approval requests in the governance workflow; Aletyx Decision Control Tower provides a central view of all tasks assigned to your role.
- [Decision Control Architecture](https://aletyx.ai/docs/decision-control/architecture.md): Core architecture of Decision Control, deployment patterns, and the extended Aletyx Decision Control Tower capabilities available in the Keystone edition.
- [Deploy on AWS Marketplace](https://aletyx.ai/docs/decision-control/aws/overview.md): Aletyx Decision Control is available on AWS Marketplace with options for both rapid evaluation and enterprise deployment.
- [Sandbox Edition](https://aletyx.ai/docs/decision-control/aws/sandbox.md): The Decision Control Sandbox provides a rapid way to test and evaluate Decision Control in your AWS environment with auto-login enabled for ease of use.
- [Container Configuration](https://aletyx.ai/docs/decision-control/deployment/container-configuration.md): Runtime requirements, environment variables, secrets, and example run commands for the Decision Control container.
- [Database Configuration](https://aletyx.ai/docs/decision-control/deployment/database-configuration.md): PostgreSQL provisioning, schema management, and connection settings for Decision Control.
- [Identity & Access Management (OIDC)](https://aletyx.ai/docs/decision-control/deployment/identity-access-management.md): Configure Microsoft Entra ID or Keycloak OIDC authentication and RBAC for Decision Control.
- [Kubernetes & OpenShift Deployment](https://aletyx.ai/docs/decision-control/deployment/kubernetes-openshift-deployment.md): Reference manifests, database connectivity, and OpenShift specifics for deploying Decision Control.
- [Decision Control — Deployment](https://aletyx.ai/docs/decision-control/deployment/overview.md): Quick-start to deploy Aletyx Decision Control locally or on Kubernetes.
- [Supported Operating Environments](https://aletyx.ai/docs/decision-control/deployment/supported-operating-environments.md): Compatibility matrix for runtimes, orchestrators, databases, identity providers, and browsers for Decision Control.
- [FAQ and Troubleshooting](https://aletyx.ai/docs/decision-control/faq-troubleshooting.md): Answers to frequently asked questions and solutions to common issues encountered when using Decision Control, organized by functional area for quick reference.
- [Authoring – AI Assistant](https://aletyx.ai/docs/decision-control/how-to/authoring-ai.md): Use the in-editor AI Assistant to build, modify, and test decision models in natural language — it can edit the diagram, generate test inputs, and answer questions about the model.
- [Authoring – Models](https://aletyx.ai/docs/decision-control/how-to/authoring-models.md): Guides covering the lifecycle of a decision model in the authoring editor: creating, importing, organizing, syncing, and publishing.
- [Authoring – Testing in the Editor](https://aletyx.ai/docs/decision-control/how-to/authoring-testing.md): Test a DMN model locally with the DMN Runner before publishing — sample inputs, multiple scenarios, and saving or loading test data.
- [Home & Global](https://aletyx.ai/docs/decision-control/how-to/home-and-global.md): How to open each Decision Control workspace from the home page and use the global Settings dialog, including connecting a Git provider.
- [Management – Execute Rules](https://aletyx.ai/docs/decision-control/how-to/management-execute.md): Run published decision models with inputs and inspect their outputs.
- [Management – Units & Models](https://aletyx.ai/docs/decision-control/how-to/management-models.md): Manage the catalog of decision units, versions, and models from the Management workbench.
- [Monitoring](https://aletyx.ai/docs/decision-control/how-to/monitoring.md): Review execution history, audit logs, and analytics for deployed models.
- [How-To Guides](https://aletyx.ai/docs/decision-control/how-to/overview.md): Task-focused guides for Decision Control — each guide answers a single "how do I…?" question with the shortest path to done.
- [Prompting](https://aletyx.ai/docs/decision-control/how-to/prompting.md): Ask about and execute decision models in natural language through the conversational assistant.
- [Integration and APIs](https://aletyx.ai/docs/decision-control/integration-and-apis.md): API reference documentation, authentication patterns, integration examples, and error handling guidance for integrating Decision Control into your enterprise architecture.
- [Decision Control](https://aletyx.ai/docs/decision-control/overview.md): Centralized governance, deployment, and management for decision services.
- [Usage Scenarios](https://aletyx.ai/docs/decision-control/usage-scenarios.md): Step-by-step tutorials for common Decision Control workflows, from creating your first DMN model through promoting it to production.
- [Decisions + GenAI](https://aletyx.ai/docs/decisions-genai.md): The Model Context Protocol (MCP) pairs deterministic Drools and DMN decisions and Kogito processes with LLMs like Claude for auditable, business-owned AI outcomes.
- [AWS Marketplace](https://aletyx.ai/docs/deployment/aws-marketplace/overview.md): Deploy Aletyx Decision Control on AWS using pre-configured AMIs from AWS Marketplace with CloudFormation-based one-click deployment.
- [Sandbox Edition](https://aletyx.ai/docs/deployment/aws-marketplace/sandbox.md): Deploy Aletyx Decision Control Sandbox with an embedded H2 database for quick evaluation, proofs-of-concept, and development environments.
- [Security](https://aletyx.ai/docs/deployment/aws-marketplace/security.md): Security guidelines for deploying and operating Aletyx Decision Control on AWS Marketplace, including network, IP, and subnet best practices.
- [SSL/HTTPS](https://aletyx.ai/docs/deployment/aws-marketplace/ssl-https.md): Configure custom domains with automatic Let's Encrypt SSL certificates for secure HTTPS access to your Decision Control deployment on AWS.
- [Troubleshooting](https://aletyx.ai/docs/deployment/aws-marketplace/troubleshooting.md): Common issues and solutions for AWS Marketplace deployments of Aletyx Decision Control, including CloudFormation stack errors.
- [Amazon ECS (Fargate / EC2)](https://aletyx.ai/docs/deployment/cloud-native/ecs.md): Deploy Aletyx Enterprise Build of Kogito and Drools workloads on Amazon ECS — Fargate or EC2 launch type, with full task-definition and service examples.
- [JVM vs Native](https://aletyx.ai/docs/deployment/cloud-native/jvm-vs-native.md): Compare JVM and GraalVM Native Image deployment for Kogito decision and process services - startup time, memory footprint, scaling, and when to choose each.
- [Cloud-Native](https://aletyx.ai/docs/deployment/cloud-native/overview.md): Deploy Aletyx Enterprise Build to Kubernetes and Red Hat OpenShift, choose between JVM and GraalVM native images, and secure the Management Console.
- [Kubernetes](https://aletyx.ai/docs/deployment/kubernetes.md): Deploy a jBPM and Kogito business process automation application to Kubernetes, with step-by-step instructions and automation scripts.
- [OpenShift](https://aletyx.ai/docs/deployment/openshift.md): This guide provides comprehensive instructions for deploying Aletyx Enterprise Build of Kogito and Drools applications to Red Hat OpenShift® environments.
- [Deployment](https://aletyx.ai/docs/deployment/overview.md): Learn how to deploy Aletyx applications across cloud-native, Kubernetes, OpenShift, Docker, and AWS environments based on your product and use case.
- [First Table](https://aletyx.ai/docs/drools/decision-tables/first-table.md): Step-by-step guide to building a Drools spreadsheet decision table in Excel - defining RuleSet, RuleTable, conditions, and actions for a shipping charges example.
- [Decision Tables](https://aletyx.ai/docs/drools/decision-tables/overview.md): Set up a Drools decision tables project - Maven dependencies (drools-decisiontables, Apache POI for Excel), the .drl.xls/.xlsx/.csv naming requirement, and project structure.
- [Running Tables](https://aletyx.ai/docs/drools/decision-tables/running.md): Load and execute a spreadsheet decision table (.drl.xlsx) in Java using the KIE API, KieContainer, and KieSession, or compile it to DRL with SpreadsheetCompiler.
- [Actions (RHS)](https://aletyx.ai/docs/drools/drl/actions.md): How to write DRL rule actions in the then section (RHS) - modifying, inserting, and removing facts, logical insertions, error handling, and best practices.
- [Building Blocks](https://aletyx.ai/docs/drools/drl/building-blocks.md): The foundational components of DRL files - packages, rule units and DataStore/DataStream/SingletonStore data sources, imports, type declarations, and rule structure.
- [Conditions (LHS)](https://aletyx.ai/docs/drools/drl/conditions.md): Write Drools rule conditions (the when section): pattern matching, OOPath, constraint operators, variable binding, not/exists/forall, and accumulate and groupby functions.
- [Data Stores](https://aletyx.ai/docs/drools/drl/data-stores.md): Drools rule unit data sources - DataStore, DataStream, and SingletonStore - for typed, reactive fact handling, plus DataHandle usage and DRL examples.
- [Decision Services](https://aletyx.ai/docs/drools/drl/decision-services.md): Build a complete DRL decision service step by step using Drools rule units, data stores, salience, JUnit tests, and a REST endpoint for loan approval.
- [OOPath](https://aletyx.ai/docs/drools/drl/oopath.md): OOPath is an object-oriented XPath extension for navigating object graphs in DRL rule conditions, replacing from and collect with concise multi-level paths in Drools 10.
- [DRL](https://aletyx.ai/docs/drools/drl/overview.md): An introduction to the Drools Rule Language (DRL): what it is, why to use it, how it compares to alternatives, and where it fits in your architecture.
- [Phreak Algorithm](https://aletyx.ai/docs/drools/drl/phreak.md): How the Phreak algorithm powers rule evaluation in Drools through lazy evaluation, segment sharing, and node/segment/rule memory, and how to write more efficient DRL rules.
- [Query System in Aletyx Enterprise Build of Drools](https://aletyx.ai/docs/drools/drl/queries.md): How the Query system extracts and analyzes data from the rule engine: traditional and Rule Unit queries, parameters, backward chaining, live queries, and best practices.
- [Rule Units](https://aletyx.ai/docs/drools/drl/rule-units.md): Organize Drools rules with Rule Units - RuleUnitData classes, DataStore, DataStream, and SingletonStore data sources, queries, OOPath, and execution with RuleUnitInstance.
- [Syntax Basics](https://aletyx.ai/docs/drools/drl/syntax-basics.md): A comprehensive overview of DRL syntax: rule structure, attributes, conditions (when), and actions (then) for authoring business rules in Drools.
- [Drools 7 EOL - What you need to know](https://aletyx.ai/docs/drools/drools-7-migration.md): Drools 7 end-of-life and migration to community Drools 10 - breaking changes from session-per-task isolation, startProcess() removal, and the retirement of KIE Server and Business Central.
- [Rule Engine](https://aletyx.ai/docs/drools/engine.md): How the Drools forward-chaining rule engine stores, matches, and executes the business rules and decision models you define.
- [Getting Started](https://aletyx.ai/docs/drools/getting-started.md): Get started with Drools: understand facts, rules, working memory, and how this forward-chaining rule engine separates business logic from application code.
- [Drools](https://aletyx.ai/docs/drools/overview.md): Overview of the Aletyx Enterprise Build of Drools - the DRL rule engine, DMN decision modeling, decision tables, and Java, REST, and event-driven integration.
- [Rule Flows](https://aletyx.ai/docs/drools/ruleflow.md): Run classic Drools rule flows on Drools 10 - Aletyx Enterprise keeps the classic KieSession startProcess API working from a KJAR with the ClassicRuleFlow metadata.
- [Troubleshooting](https://aletyx.ai/docs/drools/troubleshooting.md): Aletyx Enterprise Build of Drools provides standardized messages for DRL errors to help you troubleshoot and resolve problems in your DRL files.
- [Creating Your First Decision Table in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/drools/tutorials/decision-tables.md): Walk through creating a decision table for a shipping charges calculation system, ideal for business rules managed by business analysts and subject matter experts.
- [DRL Examples](https://aletyx.ai/docs/drools/tutorials/drl-example.md): Build a shipping charges Excel decision table (.drl.xlsx) with Drools rule units, in Aletyx Playground and a Java 17 Maven project, with RuleSet and RuleTable definitions.
- [Getting Started with Drools Rule Language (DRL) in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/drools/tutorials/first-rule.md): Create a simple loan approval rule to learn the fundamental structure and components of Drools rules, from project setup to running your first rule unit.
- [Tutorials](https://aletyx.ai/docs/drools/tutorials/overview.md): Step-by-step tutorials for building your first rules and decision tables.
- [Visual Guide](https://aletyx.ai/docs/drools/visual-guide.md): A visual guide to how the Drools rule engine processes facts and rules through working memory, the agenda, and Phreak pattern matching.
- [Frequently asked questions](https://aletyx.ai/docs/faq.md): Answers to common questions about Aletyx, its products, and how to get started.
- [What is Aletyx?](https://aletyx.ai/docs/faq/about-aletyx/what-is-aletyx.md): Aletyx is an enterprise platform combining decision intelligence and process automation on Apache KIE (Drools, Kogito, jBPM), with governance and audit built in.
- [Who builds Aletyx?](https://aletyx.ai/docs/faq/about-aletyx/who-builds-aletyx.md): Aletyx is built by the original engineers behind the Drools, jBPM, and Kogito open-source projects that power rules and process automation in regulated industries.
- [Who is Aletyx for?](https://aletyx.ai/docs/faq/about-aletyx/who-is-aletyx-for.md): Aletyx serves regulated enterprises that need auditable decisions, legacy modernization, and safe AI - for both business analysts and developers.
- [What is "Accountable AI"?](https://aletyx.ai/docs/faq/concepts/accountable-ai.md): Accountable AI is automation you can stand behind: explainable, auditable, and governed decisions grounded in explicit, versioned business logic.
- [Should enterprises choose between LLMs and symbolic AI?](https://aletyx.ai/docs/faq/concepts/beyond-llms.md): Neurosymbolic AI combines statistical LLMs with deterministic symbolic rules - using engines like Drools to validate outputs for compliance, instead of choosing one.
- [What do decision tables look like for business users?](https://aletyx.ai/docs/faq/concepts/decision-tables.md): How non-technical policy owners read and manage rules as decision tables.
- [What is the difference between DMN, DRL, and BPMN?](https://aletyx.ai/docs/faq/concepts/dmn-drl-bpmn.md): How DMN, DRL, and BPMN differ: DMN models decisions visually, DRL authors rules as code, and BPMN orchestrates processes and workflows.
- [What is the difference between Drools, Kogito, and KIE?](https://aletyx.ai/docs/faq/concepts/drools-kogito-kie.md): How Drools, Kogito, and KIE relate: KIE is the foundation platform, Drools is the rules engine, and Kogito is the cloud-native runtime that packages them.
- [What AI concepts do enterprises need to understand?](https://aletyx.ai/docs/faq/concepts/enterprise-ai-concepts.md): Key AI concepts for enterprise leaders: Symbolic AI, Machine Learning, Generative AI, LLMs, and Agentic AI - and how to pair innovation with governance.
- [How can enterprises adopt AI without losing compliance and predictability?](https://aletyx.ai/docs/faq/concepts/hybrid-intelligence.md): Hybrid Intelligence combines symbolic, generative, and agentic AI - using rule-based guardrails so enterprises can adopt AI without losing compliance or predictability.
- [How do I modernize Drools applications from old versions (7.x)?](https://aletyx.ai/docs/faq/concepts/modernize-drools.md): Modernize legacy Drools 7.x applications by moving DMN, DRL, and BPMN assets onto the cloud-native Kogito runtime - without rewrites or re-certification.
- [What is BPMN?](https://aletyx.ai/docs/faq/concepts/what-is-bpmn.md): BPMN (Business Process Model and Notation) is an OMG standard for modeling business processes visually as tasks, gateways, and events - portable and executable.
- [What is DMN?](https://aletyx.ai/docs/faq/concepts/what-is-dmn.md): DMN (Decision Model and Notation) is an OMG standard for modeling business decisions visually with decision tables and FEEL - portable and directly executable.
- [What is DRL?](https://aletyx.ai/docs/faq/concepts/what-is-drl.md): DRL (Drools Rule Language) is the native, text-based language of Drools for authoring business rules with a simple when/then structure for complex inference.
- [What is Drools?](https://aletyx.ai/docs/faq/concepts/what-is-drools.md): Drools is the open-source business rules management system (BRMS) and inference engine at the heart of KIE, authoring rules in DRL, decision tables, or DMN.
- [What is KIE?](https://aletyx.ai/docs/faq/concepts/what-is-kie.md): KIE (Knowledge Is Everything) is the umbrella Apache project and shared platform behind Drools, jBPM, and Kogito, providing common APIs, tooling, and runtime.
- [What is Kogito?](https://aletyx.ai/docs/faq/concepts/what-is-kogito.md): Kogito is the cloud-native evolution of Drools and jBPM, compiling DMN/DRL decisions and BPMN processes into container-ready Quarkus microservices.
- [Why do I need to update Drools?](https://aletyx.ai/docs/faq/concepts/why-update-drools.md): Why update Drools: old versions carry unpatched CVEs, lose support after end-of-life, and miss the performance and cloud-native gains of modern runtimes.
- [How is Aletyx deployed?](https://aletyx.ai/docs/faq/deployment-pricing-support/deployment.md): Aletyx offers cloud-native, multi-environment deployment, with builds distributed through Maven and container registries.
- [What performance gains can I expect?](https://aletyx.ai/docs/faq/deployment-pricing-support/performance.md): Aletyx reports up to 30x faster execution than standard Drools, around 20% faster processing, and roughly 300% ROI over three years, depending on workload.
- [How is Aletyx priced?](https://aletyx.ai/docs/faq/deployment-pricing-support/pricing.md): Aletyx uses a predictable flat-fee pricing model with no usage-based surprises. Contact the Aletyx team for details specific to your deployment.
- [What support is available?](https://aletyx.ai/docs/faq/deployment-pricing-support/support.md): Aletyx provides community and commercial support channels plus enterprise consulting and training services.
- [Does Aletyx integrate with AI assistants?](https://aletyx.ai/docs/faq/getting-started/ai-assistants.md): Yes - Aletyx supports the Model Context Protocol (MCP), including Claude Desktop, so AI assistants can work directly with your decisions and processes.
- [What developer tools are available?](https://aletyx.ai/docs/faq/getting-started/developer-tools.md): Aletyx developer tools include a VS Code extension, an online playground, design environments, and Maven and container-registry integration.
- [How do I get started?](https://aletyx.ai/docs/faq/getting-started/get-started.md): Start building with Aletyx through the Quickstart, persona-based learning paths, or the interactive online playground.
- [How do I invoke Kogito processes with Java APIs instead of REST?](https://aletyx.ai/docs/faq/getting-started/kogito-java-apis.md): Drive Kogito BPMN processes from Java instead of REST: disable the generated endpoints and use CDI injection with the ProcessService API for full control.
- [How can analysts update decision models without waiting on IT?](https://aletyx.ai/docs/faq/products-features/ai-assistant.md): The Aletyx AI Assistant helps analysts understand DMN models, propose changes in plain language, review edge cases, and generate test scenarios with less back-and-forth with IT.
- [How does Aletyx keep AI safe in regulated decisions?](https://aletyx.ai/docs/faq/products-features/ai-safety.md): Aletyx applies deterministic AI guardrails so LLM outputs cannot override business policy, with every decision and workflow step captured in a full audit trail.
- [What is Decision Control?](https://aletyx.ai/docs/faq/products-features/decision-control.md): Decision Control is the Aletyx decision-lifecycle capability: visual multi-version DMN authoring, governance workflows, multi-environment deployment, and natural-language testing.
- [How can I connect DMN decisions to LLMs with MCP?](https://aletyx.ai/docs/faq/products-features/dmn-mcp.md): Connect DMN decisions to LLMs automatically with the Model Context Protocol (MCP) - no custom code, with guardrails that prevent hallucinations and preserve auditability.
- [Can I modernize legacy jBPM, RHPAM, or KIE Server systems?](https://aletyx.ai/docs/faq/products-features/modernize-legacy-systems.md): Aletyx is a drop-in replacement for aging jBPM, RHPAM, and KIE Server systems, letting you modernize without rewrites, behavior changes, or re-certification.
- [What products does Aletyx offer?](https://aletyx.ai/docs/faq/products-features/products.md): Aletyx offers Decision Intelligence for governed business logic and Intelligent Process Automation for compliant, auditable end-to-end workflows.
- [Cloud Command Line Tools in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/cloud-tools.md): Install and configure the Kubernetes CLI (kubectl) and Red Hat OpenShift CLI (oc) for Aletyx Enterprise Build of Kogito and Drools.
- [Container Runtime Setup in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/container-tools.md): Choose and set up your preferred container runtime environment, Docker or Podman, for Aletyx Enterprise Build of Kogito and Drools.
- [Environment Setup in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/environment-setup.md): Set up your development environment for Aletyx Enterprise Build of Kogito and Drools, covering installation and configuration of all required tools and dependencies.
- [GitHub Integration with Aletyx Playground in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/git-configuration.md): Connect Aletyx Playground to GitHub to publish projects, synchronize changes, and deploy through traditional deployment pipelines.
- [Java Installation and Configuration in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/java-setup.md): Install and configure Java 17 for Aletyx Enterprise Build of Kogito and Drools on Windows, macOS, and Linux.
- [Configuring Maven Settings for Aletyx Enterprise Build](https://aletyx.ai/docs/getting-started/maven.md): How to configure your Maven settings.xml to access the Aletyx Maven repositories for the Aletyx Enterprise Build of Apache KIE.
- [Ubuntu Environment Setup Guide for Aletyx Enterprise Build of Kogito and Drools](https://aletyx.ai/docs/getting-started/ubuntu.md): Step-by-step instructions for setting up Docker, Java 17, Apache Maven, and Visual Studio Code on Ubuntu for Aletyx Enterprise Build of Kogito and Drools.
- [Visual Studio Code® and Aletyx Developer Tools Configuration in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/getting-started/vscode.md): Install Visual Studio Code and configure the Aletyx Developer Tools extensions (BPMN and DMN editors) on Windows, macOS, and Linux.
- [Best Practices](https://aletyx.ai/docs/guides/best-practices.md): Best practices for modeling clear, maintainable, and scalable BPMN business processes in Aletyx.
- [Decisions](https://aletyx.ai/docs/guides/decisions.md): Author, test, deploy, and govern business decisions with DMN and DRL on Aletyx - deterministic, auditable logic running on Drools and Kogito.
- [Aletyx AI Assistant](https://aletyx.ai/docs/guides/getting-started/ai-assistant.md): Subscribe to the Aletyx AI Assistant, set up your token, and start working with your decision models in plain language.
- [Exploring the Aletyx Playground](https://aletyx.ai/docs/guides/getting-started/getting-started-playground.md): Navigate the documentation and find the right starting point in the Aletyx Playground based on your role and objectives.
- [Guides](https://aletyx.ai/docs/guides/overview.md): Practical guides and lifecycle documentation for building and operating decision automation systems.
- [Processes](https://aletyx.ai/docs/guides/processes.md): Orchestrate and automate long-running business processes with BPMN on the Aletyx enterprise build of jBPM and Kogito, running cloud-natively on Quarkus.
- [BPMN Examples](https://aletyx.ai/docs/guides/tutorials/bpmn-example.md): Build an employee onboarding BPMN workflow in Aletyx Playground and a Quarkus Maven project - user and service tasks, parallel gateways, boundary timers, and Kogito REST APIs.
- [DMN Accelerators](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-accelerators.md): Accelerators in Aletyx Playground are powerful templates that transform your standalone DMN models into fully structured projects ready for deployment.
- [Advanced Deployment](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-advanced-deployment.md): Deploy DMN decision services to production - JVM vs native containers, Kubernetes scaling and HPA, CI/CD, ConfigMaps and Secrets, monitoring, and security.
- [Basic Deployment](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-basic-deployment.md): Deploy a DMN model as a containerized REST decision service using Aletyx Playground dev deployments, test it via Swagger UI, and optionally deploy to OpenShift.
- [Basic Examples](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-basic-example.md): Step-by-step tutorial building a DMN model in Aletyx Playground that calculates employee vacation days from age and years of service using decision tables and FEEL.
- [Advanced Modeling](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-deeper.md): Advanced DMN modeling with Business Knowledge Models (BKMs), custom data types, list operations, hit policies, and FEEL context expressions, built through a travel insurance pricing example.
- [Introduction to DMN](https://aletyx.ai/docs/guides/tutorials/dmn/dmn-intro.md): Introduction to DMN decision modeling - Decision Requirements Diagrams, decision tables, hit policies, FEEL expressions, and executable models in the Apache KIE ecosystem.
- [Decision Modeling](https://aletyx.ai/docs/guides/tutorials/dmn/overview.md): Guide to Decision Model and Notation (DMN) with Aletyx - from building your first decision model and using accelerators to advanced modeling, deployment, and best practices.
- [Lab: Event Driven Architecture for Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/guides/tutorials/lab-eda.md): Interact with decisions and the rule engine using Kafka topics, and create processes that interact with Kafka topics using Messages nodes.
- [Lab: Let's Orchestrate Rules and Decisions in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/guides/tutorials/lab-rules-orchestration.md): Create a BPMN process that orchestrates a DMN decision and a DRL RuleUnit to validate a driver's license and evaluate traffic violations.
- [Lab: Build a process using SAGA pattern in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/guides/tutorials/lab-saga.md): Model an order process with service tasks, gateways, and compensatory actions that simulate the SAGA pattern using a data-driven flow approach.
- [Ad-Hoc Subprocesses in Aletyx Enterprise Build of Kogito and Drools 10.1.0-aletyx](https://aletyx.ai/docs/guides/tutorials/sub-adhoc.md): Ad-hoc subprocesses provide flexibility by allowing tasks within them to be executed in a non-sequential order, for scenarios where the exact sequence of activities cannot be predetermined.
- [Regulatory Compliance](https://aletyx.ai/docs/guides/use-cases/compliance.md): Automate regulatory compliance with auditable decisions and adaptive processes across financial reporting, HIPAA healthcare, and anti-money-laundering (AML) use cases.
- [Finance Use Cases](https://aletyx.ai/docs/guides/use-cases/finance.md): Finance and banking process automation use cases - loan approval, expense reimbursement, and credit risk assessment workflows.
- [Healthcare Use Cases](https://aletyx.ai/docs/guides/use-cases/healthcare.md): Healthcare process automation use cases - patient intake and triage, insurance claims processing, and treatment authorization.
- [Human Resources Use Cases](https://aletyx.ai/docs/guides/use-cases/human-resources.md): Human resources process automation use cases - employee onboarding, performance reviews, and leave request management.
- [Insurance Claims Processing](https://aletyx.ai/docs/guides/use-cases/insurance.md): Automate property and casualty, health, and fraud-investigation insurance claims with rules-based triage, risk scoring, and straight-through processing on BPM and decision management.
- [Lending Automation](https://aletyx.ai/docs/guides/use-cases/lending.md): Lending automation use cases - commercial loan approval, mortgage processing, and credit limit increases - with rules-based decisioning and BPMN process orchestration.
- [Manufacturing Use Cases](https://aletyx.ai/docs/guides/use-cases/manufacturing.md): Manufacturing process automation use cases - supply chain management, quality control, and inventory management.
- [Customer Onboarding](https://aletyx.ai/docs/guides/use-cases/onboarding.md): Automate customer onboarding with BPMN workflows and decision rules - financial services KYC/AML, telecom service activation, and healthcare patient intake use cases.
- [Industry Use Cases](https://aletyx.ai/docs/guides/use-cases/overview.md): Domain-specific patterns and real-world applications of decision and process automation.
- [Retail Use Cases](https://aletyx.ai/docs/guides/use-cases/retail.md): Retail process automation use cases - order fulfillment, customer complaint resolution, and pricing and discount approval.
- [Welcome to Aletyx](https://aletyx.ai/docs/index.md): The platform for Accountable AI — build, test, deploy, and govern business decisions and processes.
- [KIE Server and KIE container commands in jBPM](https://aletyx.ai/docs/kie-server/commands.md): Server commands that you can send to KIE Server for server-related or container-related operations, such as retrieving server information or creating or deleting a container.
- [jBPM controller Java client API for KIE Server templates and instances](https://aletyx.ai/docs/kie-server/controller-java-api.md): Connect to the jBPM controller using REST or WebSocket protocol from your Java client application to interact with KIE Server templates, instances, and associated KIE containers.
- [jBPM controller REST API for KIE Server templates and instances](https://aletyx.ai/docs/kie-server/controller-rest-api.md): Use the jBPM controller REST API to interact with your KIE Server templates (configurations), KIE Server instances (remote servers), and associated KIE containers (deployment units) in jBPM without using the Business Central user interface.
- [Creating a KIE container](https://aletyx.ai/docs/kie-server/creating-a-kie-container.md): Once your Execution Server is registered, you can start adding KIE containers to it.
- [EJB API for KIE sessions and task services](https://aletyx.ai/docs/kie-server/ejb-api.md): jBPM provides an Enterprise JavaBeans (EJB) API that you can use for embedded use cases to access KieSession and TaskService objects remotely from an application.
- [KIE Server capabilities and extensions](https://aletyx.ai/docs/kie-server/extensions.md): The capabilities in KIE Server are determined by plug-in extensions that you can enable, disable, or further extend to meet your business needs.
- [Installing the KIE Server](https://aletyx.ai/docs/kie-server/installation.md): The KIE Server is distributed as a web application archive (WAR) file, with packagings for web containers and Java EE containers.
- [KIE Server Java client API for KIE containers and business assets](https://aletyx.ai/docs/kie-server/java-client-api.md): Connect to KIE Server using REST protocol from your Java client application to interact with KIE containers and business assets as an alternative to the KIE Server REST API.
- [Managing Containers](https://aletyx.ai/docs/kie-server/managing-containers.md): Containers within the Execution Server can be started, stopped and updated from within Business Central.
- [Overview](https://aletyx.ai/docs/kie-server/overview.md): KIE Execution Server is a modular, standalone server component that can be used to instantiate and execute rules and processes via REST, JMS and Java interfaces.
- [Performance tuning considerations with KIE Server](https://aletyx.ai/docs/kie-server/performance-tuning.md): Key concepts and suggested practices that can help you optimize KIE Server performance.
- [Prometheus metrics monitoring in jBPM](https://aletyx.ai/docs/kie-server/prometheus-monitoring.md): Prometheus is an open-source systems monitoring toolkit that you can use with jBPM to collect and store metrics related to the execution of business rules, processes, DMN models, and other jBPM assets.
- [KIE Server REST API for KIE containers and business assets](https://aletyx.ai/docs/kie-server/rest-api.md): Use the KIE Server REST API to interact with your KIE containers and business assets (such as business rules, processes, and solvers) in jBPM without using the Business Central user interface.
- [Runtime commands in jBPM](https://aletyx.ai/docs/kie-server/runtime-commands.md): Runtime commands that you can send to KIE Server for asset-related operations, such as executing all rules or inserting or retracting objects in a KIE session.
- [Securing password using key store](https://aletyx.ai/docs/kie-server/securing-passwords.md): Store KIE Server passwords in a key store instead of clear text on the disk and use them in the application.
- [KIE Server setup](https://aletyx.ai/docs/kie-server/setup.md): Set up KIE Server in managed mode with a jBPM controller or as an unmanaged standalone instance.
- [KIE Server system properties](https://aletyx.ai/docs/kie-server/system-properties.md): System properties (bootstrap switches) accepted by KIE Server to configure the behavior of the server.
- [KIE Server Task Assigning](https://aletyx.ai/docs/kie-server/task-assigning.md): Integrating OptaPlanner with jBPM to assign the human tasks produced by business processes to users as part of an optimized plan.
- [Cookie Policy](https://aletyx.ai/docs/legal/cookie-policy.md): How Aletyx, Inc. uses cookies and similar technologies on our website, how they work, and your choices regarding their use.
- [Product Features](https://aletyx.ai/docs/product/overview.md): Enterprise extensions and product-specific features
- [Quickstart](https://aletyx.ai/docs/quickstart.md): Choose the Aletyx getting-started path that matches your role - decision analyst or tech lead - and reach a working result quickly.
- [Quickstart for Decision Analysts](https://aletyx.ai/docs/quickstart/decision-analysts.md): Decision-analyst quickstart: model and test business decisions and processes visually in the Aletyx Playground, with no coding or installation required.
- [Quickstart for Tech Leads](https://aletyx.ai/docs/quickstart/tech-leads.md): Tech-lead quickstart: design enterprise-scale, deployable Aletyx automation, covering architecture styles and Kubernetes, OpenShift, or Docker deployment.
- [DMN Reference](https://aletyx.ai/docs/reference/dmn/overview.md): DMN technical reference for Aletyx - FEEL reserved words and select expression syntax, plus pointers to conceptual Decision Model and Notation documentation.
- [Reserved Words](https://aletyx.ai/docs/reference/dmn/reserved-words.md): Reference of DMN FEEL reserved words and keywords - logical, comparison, collection, and temporal operators, built-in data types, and variable naming rules to avoid conflicts.
- [Select Expression](https://aletyx.ai/docs/reference/dmn/select-reference.md): Reference for DMN Select expression types - Literal, Relation, Context, Decision Table, List, Invocation, Function, Conditional, For, Every, Some, and Filter, with FEEL examples and hit policies.
- [Reference](https://aletyx.ai/docs/reference/overview.md): Technical reference for the Aletyx Enterprise Build of Kogito and Drools - DMN reserved words, select expressions, release notes, plus curated books, blogs, and standards for DMN and BPMN.
- [Tech Stack](https://aletyx.ai/docs/tech-stack.md): The Aletyx Enterprise Build is a business automation platform on Apache KIE - Drools, jBPM, and Kogito - with modular architecture and flexible cloud or on-prem deployment.

## OpenAPI Specs

- [openapi](https://aletyx.ai/docs/api-reference/openapi.json)