diff --git a/Dockerfile b/Dockerfile
index bc57d11..890c5fb 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -20,9 +20,7 @@ COPY pyproject.toml uv.lock ./
RUN uv sync --frozen --no-dev
# Copy project files
-RUN mkdir -p docs
-COPY mkdocs.yml ./
-COPY . ./docs/
+COPY . .
# Build the MkDocs site
RUN uv run mkdocs build --strict --site-dir /app/site
diff --git a/README.md b/README.md
index c742fa6..98a13fd 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
# The AI Reliability Engineering (AIRE) Standards
[](https://creativecommons.org/licenses/by/4.0/)
-[]()
+[](https://github.com/exospherehost/ai-reliability-standards)
> **An open implementation guide for building reliable AI Agents at scale. Defining the practices for AI Reliability Engineering (AIRE).**
@@ -134,16 +134,16 @@ Security for AI agents differs from traditional software-agents are autonomous d
### 5. Operational Excellence & Team Culture
-*Establishing SLAs, error budgets, team structures, and operational practices that enable reliable AI systems to scale.*
+*Establishing performance targets, quality budgets, team structures, and operational practices that enable reliable AI systems to scale.*
Operational Excellence bridges the gap between technical architecture and organizational culture. While the first four pillars define *what* to build, this pillar defines *how* teams operate, measure, and continuously improve AI systems at scale:
-- **AI-Specific SLAs & Error Budgets** - Service Level Objectives for availability, latency, quality, safety, and efficiency; error budget policies for balancing reliability with innovation velocity
+- **AI-Specific Performance Targets & Quality Budgets** - Performance targets for cognitive accuracy, safety integrity, autonomy level, response performance, and cost efficiency; quality budget policies for balancing reliability with innovation velocity
- **Team Structure & Shared Responsibility** - Product teams own agents end-to-end; embedded AI Reliability Engineers (AIREs) with 20% time allocation; central platform team provides infrastructure
- **Progressive Autonomy Maturity Model** - Five levels of agent autonomy (L0: Human-Driven → L4: Autonomous), reducing HITL rate from 100% to <5% over time
-- **Reliability Reviews** - Weekly metric reviews, monthly postmortems, error budget tracking, SLO compliance monitoring
+- **Reliability Reviews** - Weekly metric reviews, monthly postmortems, quality budget tracking, performance target compliance monitoring
-**Key Metrics:** SLO Compliance >95%, Error Budget Remaining >25%, HITL Rate <10%, Autonomy Level L3+, Time to Autonomy <6 months
+**Key Metrics:** Performance Target Compliance >95%, Quality Budget Remaining >50%, HITL Rate <10%, Autonomy Level L3+, Time to Autonomy <6 months
📖 **[Read the full Operational Excellence guide →](docs/pillars/operational-excellence.md)**
@@ -187,19 +187,31 @@ You get to shape the future of AI reliability engineering and get recognized for
## Repository Structure
-```
-docs/
-├── getting-started.md # Adoption roadmap for organizations
-├── pillars/
-│ ├── resilient-architecture.md # Pillar 1: Fault tolerance, scaling, recovery
-│ ├── cognitive-reliability.md # Pillar 2: Accuracy, consistency, drift detection
-│ ├── quality-lifecycle.md # Pillar 3: Testing, deployment, feedback loops
-│ ├── security.md # Pillar 4: JIT access, guardrails, audit logs
-│ └── operational-excellence.md # Pillar 5: SLAs, team structure, progressive autonomy
-└── appendix/
- ├── principles.md # AIRE Principles (5 guiding tenets)
- ├── metrics-framework.md # Three-tier metrics framework
- └── glossary.md # Key terms and definitions
+This repository contains the source files for the AIRE Standards documentation and deployment infrastructure:
+
+```text
+.
+├── docs/ # MkDocs documentation source
+│ ├── index.md # Documentation homepage
+│ ├── getting-started.md # Adoption roadmap for organizations
+│ ├── principles.md # AIRE Principles (5 guiding tenets)
+│ ├── pillars/ # Core reliability pillars
+│ │ ├── resilient-architecture.md # Pillar 1: Fault tolerance, scaling, recovery
+│ │ ├── cognitive-reliability.md # Pillar 2: Accuracy, consistency, drift detection
+│ │ ├── quality-lifecycle.md # Pillar 3: Testing, deployment, feedback loops
+│ │ ├── security.md # Pillar 4: JIT access, guardrails, audit logs
+│ │ └── operational-excellence.md # Pillar 5: Performance targets, team structure, progressive autonomy
+│ └── appendix/
+│ ├── metrics-framework.md # Three-tier metrics framework
+│ └── glossary.md # Key terms and definitions
+├── assets/ # Static assets (sponsor logos, images)
+├── k8s/ # Kubernetes deployment manifests
+├── stylesheets/ # Custom CSS for documentation
+├── mkdocs.yml # MkDocs configuration
+├── Dockerfile # Container image for documentation site
+├── pyproject.toml # Python project dependencies
+├── README.md # GitHub repository homepage (this file)
+├── CONTRIBUTORS.md # Contributors registry
```
---
@@ -215,7 +227,7 @@ We welcome Pull Requests (PRs) from engineers who have solved specific reliabili
## Sponsors
-
+
Contact nikita@exosphere.host to sponsor this work.
diff --git a/assets/sponsors/exosphere.png b/docs/assets/sponsors/exosphere.png
similarity index 100%
rename from assets/sponsors/exosphere.png
rename to docs/assets/sponsors/exosphere.png
diff --git a/docs/index.md b/docs/index.md
index 36e5d93..1174876 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,7 +1,7 @@
# The AI Reliability Engineering (AIRE) Standards
[](https://creativecommons.org/licenses/by/4.0/)
-[]()
+[](https://github.com/exospherehost/ai-reliability-standards)
> **An open implementation guide for building reliable AI Agents at scale. Defining the practices for AI Reliability Engineering (AIRE).**
@@ -109,7 +109,6 @@ Operational Excellence bridges the gap between technical architecture and organi
---
-
## AIRE Principles
*Guiding tenets inspired by SRE:*
@@ -150,7 +149,6 @@ Design for autonomous operation. Human escalation is a safety net for edge cases
---
-
## Getting Started
**New to AIRE?** Start with the **[Getting Started Guide →](getting-started.md)** for a step-by-step adoption roadmap:
@@ -189,17 +187,20 @@ You get to shape the future of AI reliability engineering and get recognized for
## Repository Structure
-```
-docs/
+This documentation is built from the [ai-reliability-standards repository](https://github.com/exospherehost/ai-reliability-standards). The repository structure includes:
+
+```text
+docs/ # Documentation source files
+├── index.md # This page (documentation homepage)
├── getting-started.md # Adoption roadmap for organizations
-├── pillars/
+├── principles.md # AIRE Principles (5 guiding tenets)
+├── pillars/ # Core reliability pillars
│ ├── resilient-architecture.md # Pillar 1: Fault tolerance, scaling, recovery
│ ├── cognitive-reliability.md # Pillar 2: Accuracy, consistency, drift detection
│ ├── quality-lifecycle.md # Pillar 3: Testing, deployment, feedback loops
│ ├── security.md # Pillar 4: JIT access, guardrails, audit logs
-│ └── operational-excellence.md # Pillar 5: SLAs, team structure, progressive autonomy
+│ └── operational-excellence.md # Pillar 5: Performance targets, team structure, progressive autonomy
└── appendix/
- ├── principles.md # AIRE Principles (5 guiding tenets)
├── metrics-framework.md # Three-tier metrics framework
└── glossary.md # Key terms and definitions
```
@@ -219,7 +220,7 @@ We welcome Pull Requests (PRs) from engineers who have solved specific reliabili
-Contact nivedit@exosphere.host to sponsor this work.
+Contact nikita@exosphere.host to sponsor this work.
## License