diff --git a/.github/workflows/build-antora.yml b/.github/workflows/build-antora.yml
index 84a4c1f..592cf41 100644
--- a/.github/workflows/build-antora.yml
+++ b/.github/workflows/build-antora.yml
@@ -1,48 +1,15 @@
 # Reusable Workflow: Build Antora Documentation Site
 # This workflow can be called from other workflows to build the Antora site.
-# Includes Kroki/Mermaid services for diagram rendering.
 
 name: Build Antora Site
 
 on:
-  workflow_call:
-    inputs:
-      node-version:
-        description: 'Node.js version to use'
-        required: false
-        type: string
-        default: '24.3.0'
-      playbook-file:
-        description: 'Path to Antora playbook file'
-        required: false
-        type: string
-        default: 'antora-playbook.yml'
-    outputs:
-      build-path:
-        description: 'Path to the built site directory'
-        value: 'build/site'
+  workflow_call: {}
 
 jobs:
   build:
     runs-on: ubuntu-latest
 
-    services:
-      mermaid:
-        image: yuzutech/kroki-mermaid:latest
-        ports:
-          - 8002:8002
-      kroki:
-        image: yuzutech/kroki:latest
-        env:
-          KROKI_MERMAID_HOST: mermaid
-        ports:
-          - 8000:8000
-        options: >-
-          --health-cmd "curl -f http://localhost:8000/health || exit 1"
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-
     steps:
       - name: Checkout playbook source
         uses: actions/checkout@v6
@@ -50,13 +17,18 @@ jobs:
       - name: Setup Node.js
         uses: actions/setup-node@v6
         with:
-          node-version: ${{ inputs.node-version }}
+          node-version: 24
 
       - name: Install Antora and Dependencies
         run: npm install
 
+      - name: Run docker compose
+        uses: hoverkraft-tech/compose-action@05da55b2bb8a5a759d1c4732095044bd9018c050 # v2.4.3
+        with:
+          compose-file: './compose.yml'
+
       - name: Generate Site
-        run: npx antora generate --log-failure-level warn ${{ inputs.playbook-file }}
+        run: npm run build
 
       - name: Upload built site
         uses: actions/upload-artifact@v6
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/publish.yml
similarity index 100%
rename from .github/workflows/deploy-docs.yml
rename to .github/workflows/publish.yml
diff --git a/.gitignore b/.gitignore
index 28307a2..43cb219 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,4 @@
-/build/
-.cache/
-.idea/
-/node_modules/
+.idea
+build
+node_modules
+.cache
diff --git a/README.md b/README.md
index 98e3e99..ce30fa2 100644
--- a/README.md
+++ b/README.md
@@ -1,34 +1,25 @@
-# Antora Playbook
+# Agentic Layer Documentation
 
-A documentation site generator configuration that aggregates and publishes documentation from multiple Git repositories into a unified, searchable documentation website for the Agentic Layer ecosystem.
-
-----
-
-## Table of Contents
-
-- [Getting Started](#getting-started)
-- [Configuration](#configuration)
-- [Publishing](#publishing)
-- [Contributing](#contributing)
-
-----
+Documentation of the Agentic Layer ecosystem, built with Antora by aggregating content from multiple Git repositories.
 
 ## Getting Started
 
-### Installation
-
 Install all required dependencies:
 
-```bash
+```shell
 npm install
 ```
 
-### Starting the Documentation Build
+Start kroki backend for diagram generation:
+
+```shell
+docker compose up -d
+```
 
 Generate the documentation site:
 
-```bash
-npx antora antora-playbook.yml
+```shell
+npm run build
 ```
 
 After successful completion, the generated documentation site will be available in the `build/site` directory.
@@ -37,86 +28,4 @@ Troubleshooting tips:
 - Ensure all referenced Git repositories and branches are accessible.
 - Clear the cache if you encounter issues: `rm -rf .cache/antora`
 
-## Configuration
-
-The project is configured through the `antora-playbook.yml` file, which defines:
-
-### Site Configuration
-- **Title**: Agentic Layer Documentation
-- **URL**: https://docs.agentic-layer.ai
-- **Start Page**: home::index.adoc
-
-### Content Sources
-Source repositories are defined in `content.sources` array of `antora-playbook.yml`.
-
-### UI Configuration
-- Uses the default Antora UI theme from GitLab
-- Custom UI overrides are stored in `supplemental-ui/` directory
-- Navigation includes links to source Git repositories
-
-### Output Configuration
-- Generated site output: `build/site/`
-- Cache directory: `.cache/antora/` (for performance optimization)
-
-## Publishing
-The playbook is set up for GitHub Pages deployment defined in `./.github/workflows/deploy-docs.yml`.
-
-## Contributing
-
-### Adding New GitHub Repositories
-
-To add a new GitHub repository as a documentation source to the antora-playbook:
-
-1. **Repository Requirements**
-   - Set up the following documentation structure in your new source repository `root`: 
-      ```
-      docs
-      ├── antora.yml
-      └── modules
-          └── ROOT
-              ├── nav.adoc
-              ├── pages
-              │   └── index.adoc
-              └── partials
-                  ├── explanation.adoc
-                  ├── how-to-guides.adoc
-                  ├── reference.adoc
-                  └── tutorials.adoc
-      
-      ```   
-
-   - `antora.yml` describes the component. Example:
-     ```yaml
-      name: <COMPONENT_NAME>
-      title: <COMPONENT_TITLE>
-      version: main # or your versioning scheme
-      start_page: ROOT:index.adoc
-      nav:
-      - modules/ROOT/nav.adoc
-     ```
-
-2. **Update antora-playbook.yml**
-   - Add a new entry to the `content.sources` array
-   - Specify the GitHub repository URL
-   - Define which branches to include (use `['feature/*']` for development, `[main]` for production)
-   - Set the `start_path` if documentation is not in the root (typically `docs`)
-
-   Example:
-   ```yaml
-   - url: https://github.com/agentic-layer/your-new-repo.git
-     branches: ['feature/*']  # Change to [main] when production-ready
-     start_path: docs
-   ```
-
-3. **Test the Configuration**
-   - Run `npx antora antora-playbook.yml` locally to verify the build works
-   - Check that the new documentation appears correctly in the generated site
-   - Ensure all links and cross-references function properly
-
-
-### Development Guidelines
-
-- Follow the existing Antora playbook configuration patterns
-- Test documentation builds locally before pushing changes
-- Ensure all referenced Git repositories and branches are accessible; i.e. verfy repository access and that branches exist
-- Update branch references from `feature/*` to `main` when components are production-ready
\ No newline at end of file
+The project is configured through the `antora-playbook.yml` file.
diff --git a/antora-playbook.yml b/antora-playbook.yml
index e39e73a..0bea632 100644
--- a/antora-playbook.yml
+++ b/antora-playbook.yml
@@ -9,12 +9,11 @@ site:
 content:
   edit_url: false # Disable "Edit this page" links
   sources:
-    - url: https://github.com/agentic-layer/documentation.git
-      branches: [main]
-      start_path: home/docs
-    - url: https://github.com/agentic-layer/documentation.git
-      branches: [main]
-      start_path: architecture/docs
+    - url: ./
+      branches: HEAD
+      start_paths:
+        - home/docs
+        - architecture/docs
     - url: https://github.com/agentic-layer/agent-runtime-operator.git
       branches: [main]
       start_path: docs
diff --git a/architecture/docs/antora.yml b/architecture/docs/antora.yml
new file mode 100644
index 0000000..42d6d09
--- /dev/null
+++ b/architecture/docs/antora.yml
@@ -0,0 +1,6 @@
+name: architecture
+title: Agentic Layer Architecture
+version: main
+start_page: ROOT:index.adoc
+nav:
+- modules/ROOT/nav.adoc
\ No newline at end of file
diff --git a/architecture/docs/modules/ROOT/nav.adoc b/architecture/docs/modules/ROOT/nav.adoc
new file mode 100644
index 0000000..cc89fd1
--- /dev/null
+++ b/architecture/docs/modules/ROOT/nav.adoc
@@ -0,0 +1 @@
+include::architecture:ROOT:partial$architecture-contents.adoc[]
\ No newline at end of file
diff --git a/architecture/docs/modules/ROOT/pages/index.adoc b/architecture/docs/modules/ROOT/pages/index.adoc
new file mode 100644
index 0000000..b4b3d67
--- /dev/null
+++ b/architecture/docs/modules/ROOT/pages/index.adoc
@@ -0,0 +1,3 @@
+= Agentic Layer Architecture
+
+include::architecture:ROOT:partial$architecture-contents.adoc[]
\ No newline at end of file
diff --git a/architecture/docs/modules/ROOT/partials/architecture-contents.adoc b/architecture/docs/modules/ROOT/partials/architecture-contents.adoc
new file mode 100644
index 0000000..c26b334
--- /dev/null
+++ b/architecture/docs/modules/ROOT/partials/architecture-contents.adoc
@@ -0,0 +1,12 @@
+** xref:architecture:introduction:index.adoc[Introduction and Goals]
+// ** xref:architecture:constraints:index.adoc[Architecture Constraints]
+** xref:architecture:context:index.adoc[System Scope and Context]
+// ** xref:architecture:solution-strategy:index.adoc[Solution Strategy]
+** xref:architecture:building-blocks:index.adoc[Building Block View]
+// ** xref:architecture:runtime:index.adoc[Runtime View]
+// ** xref:architecture:deployment:index.adoc[Deployment View]
+** xref:architecture:crosscutting:index.adoc[Cross-cutting Concepts]
+// ** xref:architecture:decisions:index.adoc[Architecture Decisions]
+// ** xref:architecture:quality:index.adoc[Quality Requirements]
+// ** xref:architecture:risks:index.adoc[Risks and Technical Debts]
+** xref:architecture:glossary:index.adoc[Glossary]
\ No newline at end of file
diff --git a/architecture/docs/modules/building-blocks/pages/index.adoc b/architecture/docs/modules/building-blocks/pages/index.adoc
new file mode 100644
index 0000000..f6b8204
--- /dev/null
+++ b/architecture/docs/modules/building-blocks/pages/index.adoc
@@ -0,0 +1,169 @@
+= Building Block View
+
+== Overview
+
+The Agentic Layer architecture currently consists of three building blocks that work together to provide AI orchestration capabilities:
+
+* **Agent Runtime**: The execution environment for AI agents, including the Agent Gateway for request routing and the Agent Runtime Operator for lifecycle management
+* **AI Gateway**: The abstraction layer for LLM provider interactions, providing unified access, security, and intelligent routing
+* **Observability Dashboard**: A simple dashboard that visualizes the interaction between agents and tools in real time
+
+Over time, more and more building blocks are going to be added.
+
+== Overall Request Flow
+
+The following diagram shows how these building blocks interact during typical request processing:
+
+[plantuml, core-request-flow, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Container.puml
+left to right direction
+
+System_Ext(extFrontends, "External Frontends", "User interfaces")
+System_Ext(extAgents, "External Agents", "Third-party AI agents")
+
+Container_Boundary(k8sCluster, "Kubernetes Cluster") {
+    Container(agentGateway, "Agent Gateway", "API Gateway", "Routes requests to appropriate agents")
+    Container(agents, "AI Agents", "Agent Runtime", "Business logic and AI orchestration")
+    Container(aiGateway, "AI Gateway", "LLM Proxy", "Manages LLM provider interactions")
+    Container(obsDashboard, "Observability Dashboard", "Monitoring", "Real time visualization of agent interactions")
+}
+
+System_Ext(llmProviders, "LLM Providers", "External AI services")
+
+' Request flow
+Rel(extFrontends, agentGateway, "Sends requests", "HTTPS/API")
+Rel(extAgents, agentGateway, "Agent requests", "HTTPS/API")
+Rel(agentGateway, agents, "Routes to agent", "Internal")
+Rel(agents, aiGateway, "LLM requests", "Internal")
+Rel(aiGateway, llmProviders, "API calls", "HTTPS")
+Rel(agentGateway, obsDashboard, "Send Traces", "HTTPS")
+Rel(aiGateway, obsDashboard, "Send Traces", "HTTPS")
+Rel(agents, obsDashboard, "Send Traces", "HTTPS")
+
+LAYOUT_WITH_LEGEND()
+@enduml
+....
+
+This flow demonstrates the request processing pipeline:
+
+1. **External systems** (Frontends and Agents) send requests via HTTPS/API
+2. **Agent Gateway** receives and routes requests to appropriate agents
+3. **AI Agents** process business logic and make LLM requests
+4. **AI Gateway** handles LLM provider interactions with security and routing
+5. **Observability Dashboard** collects telemetry and provides real-time visualization
+6. **LLM Providers** process AI requests and return results
+
+== Agent Runtime
+
+The Agent Runtime building block provides the execution environment and management infrastructure for AI agents within the Kubernetes cluster.
+
+=== Components
+
+* **Agent Runtime Operator**: Kubernetes operator that manages agent lifecycles, configurations, and deployments
+* **Agent Gateway**: API gateway that routes incoming requests to agents and maps external APIs to internal agent interfaces
+* **AI Agents**: Individual agent instances that execute business logic and orchestrate AI operations
+
+=== Agent Runtime Architecture
+[plantuml, agent-runtime-view, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Container.puml
+left to right direction
+
+Container_Boundary(k8sCluster, "Kubernetes Cluster") {
+    Container(agentOperator, "Agent Runtime Operator", "K8s Operator", "Manages agent gateway lifecycle")
+    Container(agentGateway, "Agent Gateway", "API Gateway", "Routes requests to appropriate agents, maps APIs")
+    Container(agents, "AI Agents", "Agent Runtime", "Business logic and AI orchestration")
+}
+
+' Management relationships
+Rel(agentOperator, agentGateway, "Registers agents", "K8s API")
+Rel(agentOperator, agents, "Configure agents", "K8s API")
+Rel(agentGateway, agents, "Routes to agent", "Internal")
+
+LAYOUT_WITH_LEGEND()
+@enduml
+....
+
+=== Agent Runtime Responsibilities
+
+**Agent Runtime Operator** serves as the control plane for agent management:
+
+- Registers and configures agents with the Agent Gateway
+- Manages agent lifecycles, scaling, and resource allocation
+- Provides Kubernetes-native deployment and operational patterns
+
+**Agent Gateway** acts as the request entry point:
+
+- Routes requests to appropriate agents based on capabilities and load
+- Maps external APIs to internal agent interfaces
+- Provides load balancing and health checking for agent instances
+
+**AI Agents** execute the core business logic:
+
+- Process domain-specific workflows and business rules
+- Orchestrate interactions with external systems and services
+- Make intelligent decisions about when and how to use LLM capabilities
+
+== AI Gateway
+
+The AI Gateway building block abstracts interactions with multiple LLM providers, providing a unified interface with built-in security, monitoring, and intelligent routing capabilities.
+
+=== AI Gateway Architecture
+[plantuml, ai-gateway-view, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml
+left to right direction
+
+Container(agents, "AI Agents", "Agent Runtime", "Business logic and AI orchestration")
+
+Container_Boundary(aiGateway, "AI Gateway") {
+    Component(modelRouter, "Model Router", "Router", "Routes requests to appropriate LLM providers")
+    Component(guardrails, "Guardrails", "Security Layer", "Content filtering and safety checks")
+    Component(metrics, "Metrics", "Telemetry", "Collects and exports usage metrics")
+    Component(tokenMgmt, "Access Token Management", "Auth", "Manages API keys and authentication")
+}
+
+System_Ext(llmProviders, "LLM Providers", "External AI services")
+
+' AI Gateway flow
+Rel(agents, aiGateway, "LLM requests", "HTTPS/API")
+Rel(aiGateway, tokenMgmt, "Authenticates request", "Internal")
+Rel(tokenMgmt, guardrails, "Authenticated request", "Internal")
+Rel(guardrails, metrics, "Validated request", "Internal")
+Rel(metrics, modelRouter, "Tracked request", "Internal")
+Rel(modelRouter, llmProviders, "API calls", "HTTPS")
+
+LAYOUT_WITH_LEGEND()
+@enduml
+....
+
+=== AI Gateway Components and Flow
+
+The AI Gateway processes requests through a secure, monitored pipeline:
+
+**Access Token Management** handles authentication:
+
+- Manages API keys and authentication tokens for different LLM providers
+- Provides secure credential storage and rotation capabilities
+- Ensures proper authentication for all external AI service calls
+
+**AI Guardrails** provides security and safety controls:
+
+- Content filtering and safety checks for both input and output
+- Policy enforcement based on organizational security requirements
+- Prevents malicious or inappropriate content from reaching LLM providers
+
+**Metrics** component enables comprehensive monitoring:
+
+- Collects usage statistics, performance metrics, and cost tracking
+- Exports telemetry data to observability infrastructure
+- Provides insights into AI usage patterns and provider performance
+
+**Model Router** manages intelligent LLM routing:
+
+- Routes requests to appropriate LLM providers based on capabilities, cost, and availability
+- Provides failover and load balancing across multiple providers
diff --git a/architecture/docs/modules/constraints/pages/index.adoc b/architecture/docs/modules/constraints/pages/index.adoc
new file mode 100644
index 0000000..657e40b
--- /dev/null
+++ b/architecture/docs/modules/constraints/pages/index.adoc
@@ -0,0 +1,3 @@
+= Architecture Constraints
+
+TBD
diff --git a/architecture/docs/modules/context/pages/index.adoc b/architecture/docs/modules/context/pages/index.adoc
new file mode 100644
index 0000000..4331e30
--- /dev/null
+++ b/architecture/docs/modules/context/pages/index.adoc
@@ -0,0 +1,85 @@
+= System Scope and Context
+
+== Business Context
+
+The Agentic Layer operates within a broader enterprise ecosystem, serving as a control plane that coordinates AI agents and manages their interactions with external systems. The system is designed to integrate into existing enterprise infrastructure rather than to replace it, providing AI orchestration capabilities while maintaining organizational sovereignty over data and operations.
+
+=== System Boundary
+
+The Agentic Layer Platform Boundary defines the scope of components under direct control of the AI orchestration system. This includes all Kubernetes-native components for agent management, AI gateway services, governance tools, and operational interfaces. External systems maintain their independence while benefiting from standardized AI integration patterns.
+
+== System Context Overview
+
+[plantuml, system-context, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Context.puml
+
+Person_Ext(users, "Users", "End users")
+System_Ext(frontends, "External Frontends", "Web/mobile/other UI interacting with users")
+System_Ext(extAgents, "External Agents", "Agents running outside the platform")
+System(agentic, "Agentic Layer", "Orchestrates AI interactions and external integrations")
+System_Ext(llm, "LLM Provider", "External Large Language Model Providers (e.g., OpenAI, Gemini)")
+System_Ext(obs, "Observability Stack", "Monitoring & logging")
+System_Ext(audit, "Audit Trail", "Stores audit records")
+
+Rel(users, frontends, "Uses")
+Rel(users, extAgents, "Uses")
+Rel(frontends, agentic, "Connects to")
+Rel(extAgents, agentic, "Connects to")
+Rel(agentic, llm, "Sends prompt/receives result")
+Rel_Down(agentic, obs, "Sends telemetry")
+Rel_Down(agentic, audit, "Sends audit events")
+
+LAYOUT_WITH_LEGEND()
+@enduml
+....
+
+== External Actors and Systems
+
+=== Users
+**End users** who interact with AI-powered applications and services. These users may access AI capabilities through various interfaces including web applications, mobile apps, or direct API integrations.
+
+=== External Frontends
+**Web, mobile, and other user interfaces** that provide user-facing applications leveraging AI capabilities. These frontends connect to the Agentic Layer to access coordinated AI services while maintaining their own user experience and business logic.
+
+=== External Agents
+**AI agents running outside the platform** that need to integrate with or leverage the centralized AI orchestration capabilities. These may include existing AI systems, specialized agents, or third-party AI services that benefit from the governance and routing provided by the Agentic Layer.
+
+=== LLM Providers
+**External Large Language Model providers** such as OpenAI, Google Gemini, Anthropic Claude, and other AI service providers. The Agentic Layer abstracts differences between these providers through the AI Gateway/Model Router component.
+
+=== Observability Stack
+**External monitoring and logging infrastructure** that receives telemetry data from the Agentic Layer. This typically includes enterprise monitoring solutions, log aggregation systems, and performance monitoring tools.
+
+=== Audit Trail
+**External audit and compliance systems** that store audit records for regulatory compliance, security monitoring, and governance requirements. This ensures audit data is preserved independently of the operational platform.
+
+== System Interactions
+
+=== User Interactions
+Users interact with the system through two primary paths:
+
+- **Via External Frontends**: Users access AI capabilities through web applications, mobile apps, or other user interfaces that connect to the Agentic Layer
+- **Via External Agents**: Users may work with external AI agents that leverage the Agentic Layer's orchestration capabilities
+
+=== AI Service Integration
+The **Agentic Layer orchestrates AI interactions** by:
+
+- Sending prompts and receiving results from LLM providers through the AI Gateway/Model Router
+- Providing unified access to multiple AI service providers with intelligent routing and failover
+- Managing authentication, rate limiting, and usage tracking across providers
+
+=== Operational Data Flows
+The system maintains operational transparency through:
+
+- **Telemetry Data**: Comprehensive metrics, performance data, and system health information sent to observability infrastructure
+- **Audit Events**: Complete records of AI operations, decisions, and access patterns sent to audit systems for compliance and security monitoring
+
+== Technical Context
+
+The Agentic Layer serves as an **integration hub** rather than a data repository. It orchestrates interactions between various systems while maintaining clear separation of concerns:
+
+- **Stateless Operations**: The platform focuses on coordination and routing rather than data storage
+- **API-First Integration**: All interactions occur through well-defined APIs and standard protocols
+- **Kubernetes-Native Deployment**: All components leverage Kubernetes primitives for scaling, service discovery, and operational management
diff --git a/architecture/docs/modules/crosscutting/pages/index.adoc b/architecture/docs/modules/crosscutting/pages/index.adoc
new file mode 100644
index 0000000..1478c99
--- /dev/null
+++ b/architecture/docs/modules/crosscutting/pages/index.adoc
@@ -0,0 +1,93 @@
+= Cross-cutting Concepts
+
+== Capabilities and Plugins Principle
+
+The Agentic Layer architecture embraces a fundamental design principle of **capabilities and plugins** that enables flexible switching of implementations for various system aspects. This approach promotes modularity, extensibility, and vendor independence across all major components.
+
+=== Core Principle
+
+The capabilities and plugins pattern allows the system to define stable interfaces while supporting multiple implementations. Each capability represents a well-defined set of functionality that can be fulfilled by different plugins, enabling runtime or deployment-time selection of the most appropriate implementation.
+
+[plantuml, capabilities-plugins-principle, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml
+
+Container_Boundary(system, "Agentic Layer System") {
+    Component(capability, "Capability Interface", "Contract", "Defines required functionality")
+    Component(plugin1, "Plugin A", "Implementation", "First implementation option")
+    Component(plugin2, "Plugin B", "Implementation", "Alternative implementation")
+    Component(plugin3, "Plugin C", "Implementation", "Third-party implementation")
+}
+
+Rel(capability, plugin1, "implements", "")
+Rel(capability, plugin2, "implements", "")
+Rel(capability, plugin3, "implements", "")
+
+note right of capability : Stable interface\nallows switching\nbetween implementations
+
+@enduml
+....
+
+=== Key Benefits
+
+**Implementation Independence**: Components depend on capabilities rather than specific implementations, reducing coupling and increasing flexibility.
+
+**Vendor Neutrality**: Avoid vendor lock-in by supporting multiple implementations of the same capability.
+
+**Runtime Flexibility**: Switch between implementations based on operational requirements, performance characteristics, or cost considerations.
+
+**Extensibility**: Add new implementations without modifying existing code or disrupting running systems.
+
+**Testing and Development**: Use different implementations for testing, development, and production environments.
+
+=== Application Areas
+
+==== Exemplary Agent Gateway Implementations
+
+The Agent or AI Gateway capability could support multiple reverse proxy and API gateway implementations:
+
+* **Nginx**: Traditional reverse proxy for high-performance routing
+* **Envoy**: Modern service mesh proxy with advanced traffic management
+* **Kong**: API gateway with rich plugin ecosystem
+* **Istio Gateway**: Kubernetes-native gateway with service mesh integration
+
+[plantuml, agent-gateway-plugins, format="svg", link="self", window="_blank"]
+....
+@startuml
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml
+
+Container_Boundary(agentRuntime, "Agent Runtime") {
+    Component(gatewayCapability, "Gateway Capability", "Interface", "Request routing and load balancing")
+    Component(nginxPlugin, "Nginx Plugin", "Implementation", "Traditional reverse proxy")
+    Component(envoyPlugin, "Envoy Plugin", "Implementation", "Service mesh proxy")
+    Component(kongPlugin, "Kong Plugin", "Implementation", "API gateway with plugins")
+    Component(istioPlugin, "Istio Plugin", "Implementation", "K8s-native gateway")
+}
+
+Rel(gatewayCapability, nginxPlugin, "implements", "")
+Rel(gatewayCapability, envoyPlugin, "implements", "")
+Rel(gatewayCapability, kongPlugin, "implements", "")
+Rel(gatewayCapability, istioPlugin, "implements", "")
+
+@enduml
+....
+
+
+=== Implementation Guidelines
+
+==== Kubernetes-Native Capabilities
+
+The Agentic Layer leverages **Kubernetes Custom Resource Definitions (CRDs)** as the interface definition mechanism for capabilities. Each capability is defined as a CRD that establishes the contract and configuration schema.
+
+==== Plugin Definition through Custom Resources
+
+Plugins are defined and configured using **Custom Resources (CRs)** that correspond to their capability CRDs. This approach provides:
+
+* **Declarative Configuration**: Plugin instances are declared through Kubernetes manifests
+* **Kubernetes-Native Management**: Leverage kubectl, operators, and existing K8s tooling
+* **Version Control**: Plugin configurations are stored as code alongside infrastructure definitions
+
+==== Standard Transport APIs
+
+At the transport layer, the system uses **standard APIs** to ensure broad compatibility and interoperability.
diff --git a/architecture/docs/modules/decisions/pages/index.adoc b/architecture/docs/modules/decisions/pages/index.adoc
new file mode 100644
index 0000000..b0822b1
--- /dev/null
+++ b/architecture/docs/modules/decisions/pages/index.adoc
@@ -0,0 +1,3 @@
+= Architecture Decisions
+
+TBD
diff --git a/architecture/docs/modules/deployment/pages/index.adoc b/architecture/docs/modules/deployment/pages/index.adoc
new file mode 100644
index 0000000..f6b4cd5
--- /dev/null
+++ b/architecture/docs/modules/deployment/pages/index.adoc
@@ -0,0 +1,3 @@
+= Deployment View
+
+TBD
diff --git a/architecture/docs/modules/glossary/pages/index.adoc b/architecture/docs/modules/glossary/pages/index.adoc
new file mode 100644
index 0000000..69c892f
--- /dev/null
+++ b/architecture/docs/modules/glossary/pages/index.adoc
@@ -0,0 +1,89 @@
+= Glossary
+
+This glossary defines the key terms and concepts used throughout the Agentic Layer architecture documentation.
+
+== A
+
+**Agent Catalog**::
+Centralized registry and management system for discovering, versioning, and managing the lifecycle of AI agents within the platform. Provides metadata, documentation, and operational status for all registered agents.
+
+**Agent Gateway**::
+Entry point and routing component that handles incoming requests and distributes them to appropriate AI agents based on capabilities, availability, and load balancing policies.
+
+**Agent Runtime / Agent Orchestration**::
+Kubernetes-native execution environment that manages the lifecycle, scaling, and resource allocation of AI agents. Provides automated deployment, scaling, and monitoring of agent instances.
+
+**Agentic Layer**::
+Core platform layer containing the foundational components for AI agent orchestration, including AI Gateway, Agent Runtime, Connectors & Tools, and governance components. Built on Kubernetes infrastructure.
+
+**Agentic Workforce Management**::
+Component responsible for coordinating and orchestrating complex AI workflows across multiple agents and organizational boundaries. Manages task distribution, workflow execution, and inter-agent communication.
+
+**Agentic Workforces**::
+Application-level implementations of AI agent systems designed for specific enterprise use cases, such as Insurance Claims Processing, Healthcare Operations, or Procurement workflows.
+
+**AI Gateway / Model Router**::
+Unified interface component that abstracts interactions with multiple Large Language Model (LLM) providers. Provides intelligent routing, failover capabilities, and consistent API interfaces across different AI service providers.
+
+**AI Guardrails**::
+Security and safety component that implements content filtering, safety checks, access controls, and policy enforcement for AI interactions to ensure compliant and secure operations.
+
+**Audit Trail**::
+Comprehensive logging and tracking system that records all AI operations, decisions, and interactions for regulatory compliance, security monitoring, and operational transparency.
+
+== B
+
+**BYOC (Bring Your Own Cloud)**::
+Deployment model that allows organizations to run the Agentic Layer components within their own cloud infrastructure, maintaining full control over data, security, and compliance requirements.
+
+== C
+
+**Cloud-Native Architecture**::
+Design approach leveraging cloud computing principles, including containerization, microservices, dynamic orchestration, and declarative APIs. Built specifically for cloud environments with Kubernetes as the foundation.
+
+**Compliance Dashboard**::
+Management interface providing real-time monitoring and reporting for regulatory compliance requirements including EU AI Act, GDPR, and industry-specific standards.
+
+**Connectors & Tools**::
+Integration components that enable seamless connectivity between the Agentic Layer and external systems, databases, APIs, and third-party services.
+
+**Control Plane for Intelligent Workloads**::
+The Agentic Layer's primary function as a management and orchestration system for AI-driven processes and autonomous agents within enterprise environments.
+
+== D
+
+**Data Sovereignty**::
+The concept of maintaining complete control and governance over data location, processing, and access within defined jurisdictional and organizational boundaries.
+
+== K
+
+**Kubernetes Platform**::
+The foundational infrastructure layer providing container orchestration, scaling, resource management, and service discovery for all Agentic Layer components.
+
+**Kubernetes-Native**::
+Architecture and components designed specifically to leverage Kubernetes primitives, APIs, and operational patterns for deployment, scaling, and management.
+
+== L
+
+**LLM (Large Language Model)**::
+AI models trained on vast amounts of text data to understand and generate human-like text. Examples include GPT, Gemini, Claude, and other foundation models accessed through the AI Gateway.
+
+== M
+
+**Multi-Framework Runtime**::
+Execution environment that supports agents built with different AI frameworks and technologies while providing consistent operational characteristics and management interfaces.
+
+== O
+
+**Observability Dashboard**::
+Operational interface providing comprehensive monitoring, metrics visualization, distributed tracing, and system health information for the entire Agentic Layer platform.
+
+== T
+
+**Testbench**::
+Integrated testing framework providing automated validation, performance testing, and quality assurance capabilities for AI agents before production deployment.
+
+== V
+
+**Vendor Lock-in**::
+Dependency on a specific vendor's proprietary technologies that makes switching to alternatives difficult or costly. The Agentic Layer's open-source and agnostic design prevents vendor lock-in.
diff --git a/architecture/docs/modules/introduction/assets/images/agentic-layer.jpg b/architecture/docs/modules/introduction/assets/images/agentic-layer.jpg
new file mode 100644
index 0000000..bd3e757
Binary files /dev/null and b/architecture/docs/modules/introduction/assets/images/agentic-layer.jpg differ
diff --git a/architecture/docs/modules/introduction/pages/index.adoc b/architecture/docs/modules/introduction/pages/index.adoc
new file mode 100644
index 0000000..602de6b
--- /dev/null
+++ b/architecture/docs/modules/introduction/pages/index.adoc
@@ -0,0 +1,111 @@
+= Introduction and Goals
+
+== Purpose and Scope
+
+The Agentic Layer serves as a control plane for intelligent workloads, providing a comprehensive suite of components that coordinate and orchestrate AI agents within enterprise environments. The system is architected as modular, reusable components built on a **Kubernetes Platform** foundation, enabling integration into existing enterprise infrastructure while maintaining operational sovereignty and compliance requirements.
+
+The architecture is structured in multiple layers built on Kubernetes:
+
+* **Kubernetes Platform**: The foundational infrastructure layer providing container orchestration, scaling, and resource management
+* **Agentic Layer**: Core platform components including AI Gateway/Model Router, Agent Runtime Agent Orchestration, Connectors & Tools, Agentic Workforce Management, AI Guardrails, Testbench, Audit Trail, and Compliance Dashboard
+* **Agentic Workforces**: Application-level agent implementations for specific use cases (e.g., Insurance Claims Processing, Healthcare Operations, Procurement)
+* **Management Interface**: Operational components including Agent Gateway, Agent Catalog, and Observability Dashboard for system administration
+
+image::agentic-layer.jpg[Agentic Layer Architecture Overview,align=center]
+
+=== Implementation Status
+
+In the architecture overview diagram above:
+
+* **Green boxes** represent existing components, implemented in an initial version
+* **Yellow boxes** represent planned components that are not yet implemented
+
+This color coding provides visibility into the current development status and planned roadmap for the Agentic Layer platform.
+
+== Key Advantages
+
+The Agentic Layer provides several architectural advantages for enterprise AI deployments:
+
+=== Data Sovereignty and Infrastructure Control
+**Operational Independence and Regulatory Compliance**
+
+The system enables complete sovereignty over data, models, and infrastructure through support for both cloud and on-premise deployments. This design ensures sensitive data and AI processing remain within defined infrastructure boundaries while meeting data residency and regulatory requirements across multiple jurisdictions.
+
+=== Orchestrated Workflow Integration
+**Enterprise-Scale Process Coordination**
+
+The architecture transforms isolated AI agent invocations into coordinated, enterprise-wide processes. The platform handles distribution, control, and monitoring of agent workforces, enabling complex AI workflow orchestration across organizational boundaries.
+
+=== Technology Agnosticism and Open Standards
+**Vendor Independence and Architectural Flexibility**
+
+Built on open-source principles and agnostic design patterns, the system prevents vendor lock-in while providing transparency into system operations. The architecture supports free choice of cloud providers, AI models, and integration tools, ensuring adaptability to evolving technology landscapes.
+
+=== Regulatory Compliance and Governance
+**Built-in Audit and Security Framework**
+
+The system incorporates comprehensive governance, transparency, and audit capabilities from the architectural foundation. Built-in compliance with regulations including EU AI Act and GDPR through comprehensive logging, security controls with integrated guardrails, and adherence to industry security standards.
+
+== Technical Features & Properties
+
+The system architecture incorporates the following technical characteristics to support enterprise-grade AI operations:
+
+=== Kubernetes-Native Deployment Flexibility
+**Cloud-Native Architecture with BYOC and On-Premise Support**
+
+Built as a Kubernetes-native platform, the architecture supports flexible deployment models including public cloud, private cloud, and on-premise environments. The Kubernetes foundation provides container orchestration, scaling, and resource management while enabling organizations to maintain infrastructure control and meet data residency and regulatory compliance requirements.
+
+=== Centralized Governance Framework
+**Agent Catalog and Lifecycle Management**
+
+A comprehensive governance framework provides centralized agent catalog functionality for discovery, versioning, and lifecycle management. This enables organizational visibility and control across the entire agent ecosystem with standardized management interfaces.
+
+=== Model Abstraction Layer
+**LLM Provider Independence**
+
+An agnostic model router abstracts differences between Large Language Model providers, providing intelligent routing and failover capabilities. This design enables seamless provider switching while maintaining consistent interfaces and operational behavior.
+
+=== Observability and Audit Infrastructure
+**Comprehensive Monitoring and Compliance Logging**
+
+Built-in observability infrastructure provides comprehensive metrics collection, distributed tracing, and audit logging capabilities. This supports both operational monitoring requirements and regulatory compliance through complete audit trail maintenance.
+
+=== Multi-Framework Runtime Support
+**Heterogeneous Agent Execution Environment**
+
+The runtime environment supports multiple agent frameworks and execution patterns within a unified operational model. This enables deployment of agents built with different frameworks while maintaining consistent operational characteristics and management interfaces.
+
+=== Integrated Testing Framework
+**Agent Validation and Quality Assurance**
+
+A comprehensive testing framework provides agent validation, performance testing, and quality assurance capabilities. This ensures agent reliability and performance characteristics before production deployment through automated testing pipelines.
+
+=== Standards-Based Architecture
+**Interoperability and Forward Compatibility**
+
+The architecture is built on open standards and protocols to ensure long-term interoperability and prevent vendor lock-in. This standards-based approach maintains architectural flexibility as AI technologies evolve.
+
+== Requirements Overview
+
+=== Agent Management and Orchestration
+* **Agent Runtime / Agent Orchestration**: Kubernetes-native execution of agents with automated scaling and resource management
+* **Agent Catalog**: Centralized discovery, versioning, and lifecycle management of agent instances
+* **Agentic Workforce Management**: Coordinate complex AI workflows across organizational boundaries
+* **Agent Gateway**: Handle routing and load distribution for concurrent agent requests
+
+=== Model and Provider Integration
+* **AI Gateway / Model Router**: Unified interface with intelligent routing across multiple LLM providers
+* **Connectors & Tools**: Seamless integration with external systems and AI service providers
+* **Provider Agnostic Operations**: Abstract differences between AI service providers while maintaining operational consistency
+
+=== Governance and Compliance
+* **Audit Trail**: Generate complete audit trails for regulatory compliance and operational monitoring
+* **AI Guardrails**: Integrated content filtering, safety checks, and access management systems
+* **Compliance Dashboard**: Built-in monitoring for EU AI Act, GDPR, and industry-specific standards
+* **Testbench**: Agent validation, performance testing, and reliability verification capabilities
+
+=== Deployment and Operations
+* **Kubernetes-Native Deployment**: Cloud-native support for BYOC, public cloud, private cloud, and on-premise environments
+* **Observability Dashboard**: Comprehensive metrics collection, distributed tracing, and operational monitoring
+* **Dynamic Configuration**: Environment-specific configuration management with Kubernetes-native runtime updates
+* **Standards-Based Integration**: Open standards and protocols for enterprise system interoperability
diff --git a/architecture/docs/modules/quality/pages/index.adoc b/architecture/docs/modules/quality/pages/index.adoc
new file mode 100644
index 0000000..9a54a61
--- /dev/null
+++ b/architecture/docs/modules/quality/pages/index.adoc
@@ -0,0 +1,3 @@
+= Quality Requirements
+
+TBD
diff --git a/architecture/docs/modules/risks/pages/index.adoc b/architecture/docs/modules/risks/pages/index.adoc
new file mode 100644
index 0000000..d6e1aad
--- /dev/null
+++ b/architecture/docs/modules/risks/pages/index.adoc
@@ -0,0 +1,3 @@
+= Risks and Technical Debts
+
+TBD
diff --git a/architecture/docs/modules/runtime/pages/index.adoc b/architecture/docs/modules/runtime/pages/index.adoc
new file mode 100644
index 0000000..5f6db12
--- /dev/null
+++ b/architecture/docs/modules/runtime/pages/index.adoc
@@ -0,0 +1,3 @@
+= Runtime View
+
+TBD
diff --git a/architecture/docs/modules/solution-strategy/pages/index.adoc b/architecture/docs/modules/solution-strategy/pages/index.adoc
new file mode 100644
index 0000000..47d7a46
--- /dev/null
+++ b/architecture/docs/modules/solution-strategy/pages/index.adoc
@@ -0,0 +1,37 @@
+= Solution Strategy
+
+////
+Key Questions to Answer:
+
+== 1. Architectural Approach
+- Why choose a component-based architecture instead of a monolithic AI platform?
+- What drove the layered design (Kubernetes → Agentic Layer → Workforces → Management)?
+- How does this architecture achieve the 4 key advantages (sovereignty, agnosticity, compliance, extensibility)?
+
+== 2. Technology Decisions
+- Why Kubernetes as the foundational platform? (vs. other orchestration platforms)
+- Why the AI Gateway/Model Router pattern for LLM abstraction?
+- What drove the multi-framework runtime approach?
+- Why open-source over proprietary solutions?
+
+== 3. Quality Goal Achievement
+- How does the architecture deliver data sovereignty and compliance?
+- What patterns enable vendor agnosticism and prevent lock-in?
+- How does the design support enterprise-scale orchestration?
+- What makes it audit-ready and governance-friendly?
+
+== 4. Integration Strategy
+- How do components integrate into existing enterprise platforms?
+- What are the key deployment patterns (BYOC, on-premise, hybrid)?
+- How does the Agent Catalog and governance model work?
+
+== Information Needed:
+1. Design Philosophy: Core principles driving architectural decisions
+2. Technology Rationale: Why Kubernetes specifically? Other platforms considered?
+3. Component Evolution: How did you arrive at these specific components?
+4. Enterprise Integration: Typical integration scenarios being targeted
+5. Open Source Strategy: What drove the open source decision?
+6. Quality Tradeoffs: Architectural tradeoffs made to achieve key advantages?
+
+TBD - Content to be developed based on above questions
+////
diff --git a/compose.yml b/compose.yml
new file mode 100644
index 0000000..26a9a3b
--- /dev/null
+++ b/compose.yml
@@ -0,0 +1,11 @@
+services:
+  mermaid:
+    image: yuzutech/kroki-mermaid:latest
+  kroki:
+    image: yuzutech/kroki:latest
+    environment:
+      KROKI_MERMAID_HOST: mermaid
+    ports:
+      - "8000:8000"
+    healthcheck:
+      test: [ "CMD", "curl", "-f", "http://localhost:8000/health", "||", "exit", "1" ]
diff --git a/home/docs/antora.yml b/home/docs/antora.yml
new file mode 100644
index 0000000..17d1d85
--- /dev/null
+++ b/home/docs/antora.yml
@@ -0,0 +1,6 @@
+name: home
+title: Agentic Layer
+version: main
+start_page: ROOT:index.adoc
+nav:
+- modules/ROOT/nav.adoc
\ No newline at end of file
diff --git a/home/docs/modules/ROOT/nav.adoc b/home/docs/modules/ROOT/nav.adoc
new file mode 100644
index 0000000..111258a
--- /dev/null
+++ b/home/docs/modules/ROOT/nav.adoc
@@ -0,0 +1,18 @@
+.Tutorials
+** xref:tutorials:showcase-news.adoc[]
+
+.How-to guides
+* xref:agent-runtime-operator:agent-runtime:how-to-guide.adoc[]
+* xref:agent-runtime-operator:agents:how-to-guide.adoc[]
+
+* xref:agent-runtime-operator:agent-gateways:how-to-guide.adoc[]
+* xref:agent-runtime-operator:ai-gateways:how-to-guide.adoc[]
+
+* xref:agent-gateway-krakend-operator:operator:how-to-guide.adoc[]
+* xref:ai-gateway-litellm-operator:operator:how-to-guide.adoc[]
+
+.Reference
+* xref:agent-runtime-operator:agent-runtime:reference.adoc[]
+
+.Explanation
+** xref:architecture::index.adoc[]
diff --git a/home/docs/modules/ROOT/pages/index.adoc b/home/docs/modules/ROOT/pages/index.adoc
new file mode 100644
index 0000000..c3cd9ea
--- /dev/null
+++ b/home/docs/modules/ROOT/pages/index.adoc
@@ -0,0 +1,57 @@
+= Agentic Layer Documentation
+
+Welcome to the Agentic Layer documentation hub.
+This platform provides a comprehensive suite of tools and services for building, deploying, and monitoring agentic systems at scale.
+
+== Tutorials
+
+****
+Guided, step-by-step lessons.
+Each tutorial will walk you through building a small, complete project from scratch to give you hands-on experience.
+****
+
+=== xref:tutorials:showcase-news.adoc[]
+
+This tutorial will guide you through setting up the Agentic AI News Showcase in a local Kubernetes cluster.
+The showcase demonstrates multi-agent AI communication using the Agentic Layer platform.
+
+== How-to guides
+
+****
+Collection of practical recipes to solve specific, common problems.
+Each guide is a focused checklist of steps to accomplish a single task.
+****
+// Agent Runtime
+=== xref:agent-runtime-operator:agent-runtime:how-to-guide.adoc[]
+
+=== xref:agent-runtime-operator:agents:how-to-guide.adoc[]
+
+// AI Gateways
+=== xref:ai-gateway-litellm-operator:operator:how-to-guide.adoc[]
+
+=== xref:agent-runtime-operator:ai-gateways:how-to-guide.adoc[]
+
+// Agent Gateways
+=== xref:agent-gateway-krakend-operator:operator:how-to-guide.adoc[]
+
+=== xref:agent-runtime-operator:agent-gateways:how-to-guide.adoc[]
+
+== Reference
+
+****
+Technical encyclopedia.
+It contains detailed, factual information about the inner workings of our system, including APIs, configuration options, and message schemas.
+****
+
+=== xref:agent-runtime-operator:agent-runtime:reference.adoc[]
+
+== Explanation
+
+****
+High-level discussions on the "why" behind our project.
+Here you'll find articles on our architecture, key concepts, and design decisions.
+****
+
+=== xref:architecture::index.adoc[]
+
+This section provides an in-depth look at the architecture of the Agentic Layer platform, including its components, interactions, and deployment models.
diff --git a/home/docs/modules/tutorials/pages/showcase-news.adoc b/home/docs/modules/tutorials/pages/showcase-news.adoc
new file mode 100644
index 0000000..e4fef7f
--- /dev/null
+++ b/home/docs/modules/tutorials/pages/showcase-news.adoc
@@ -0,0 +1 @@
+include::showcase-news:ROOT:partial$tutorial.adoc[]
diff --git a/package-lock.json b/package-lock.json
index ba15aac..3694992 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -1,10 +1,13 @@
 {
-  "name": "antora-playbook",
+  "name": "agentic-layer-docs",
+  "version": "1.0.0",
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   "requires": true,
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+      "dev": true,
       "license": "MIT",
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+      "dev": true,
       "license": "MIT",
       "engines": {
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+      "dev": true,
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diff --git a/package.json b/package.json
index 9556266..6fa5e61 100644
--- a/package.json
+++ b/package.json
@@ -1,5 +1,11 @@
 {
-  "dependencies": {
+  "name": "agentic-layer-docs",
+  "version": "1.0.0",
+  "description": "Agentic Layer Documentation",
+  "scripts": {
+    "build": "antora generate --log-failure-level warn antora-playbook.yml"
+  },
+  "devDependencies": {
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     "@antora/site-generator": "^3.1.12",
     "asciidoctor-kroki": "^0.18.1"