An open implementation guide for building reliable AI Agents at scale. Defining the practices for AI Reliability Engineering (AIRE).
As AI systems move from "experimental" prototypes to "mission-critical" production environments, reliability has emerged as the single biggest barrier to adoption.
This repository serves as the Open Standard for AI Reliability Engineering (AIRE). It documents the architectural patterns, testing frameworks, and operational practices that engineering teams use to achieve production-grade reliability in non-deterministic systems.
It is not a theoretical academic paper. It is a living collection of "Success Patterns" gathered from practitioners running agents at scale.
Guiding tenets inspired by SRE:
These five principles define the philosophical foundation of AIRE. They inform the practices detailed in the five pillars and help teams make trade-off decisions when designing reliable AI systems.
Accept that identical inputs will produce variable outputs. Design systems that succeed despite variance, not systems that assume consistency.
Key Insight: AI systems are probabilistic reasoners. Don't try to make them deterministic-build resilience around their non-determinism through structured outputs, guardrails, and fallback paths.
Reliability competes with velocity for engineering resources. Treat it as a first-class product requirement with explicit budgets, not an afterthought.
Key Insight: Allocate dedicated engineering time (e.g., 20% of sprints) to reliability work: golden dataset updates, eval pipeline maintenance, incident reviews.
If you cannot quantify the reliability of your AI system, you do not have a reliable AI system. Intuition is not evidence.
Key Insight: Track concrete metrics (hallucination rate <0.1%, HITL rate <10%, uptime >99.9%). Block deployments if metrics degrade.
Every failure should preserve context, enable recovery, and generate learnings. Silent failures are unacceptable.
Key Insight: Save checkpoints, log Chain of Thought reasoning, return user-friendly errors, and ensure workflows can resume after crashes.
Design for autonomous operation. Human escalation is a safety net for edge cases, not a substitute for robust engineering.
Key Insight: Reduce HITL rate over time through active learning. Start at 100% human review, target <10% through continuous improvement.
📖 Read the detailed AIRE Principles guide →
We define the reliability of an Agentic System through five core pillars:
Building systems that gracefully handle failures, scale under load, and recover from errors.
Resilient architecture establishes the structural foundation for reliable AI systems. It encompasses:
- Elastic Auto-Scaling - Horizontal and vertical scaling strategies for unpredictable AI workloads
- State Management - Checkpoint-based recovery enabling workflows to resume from last checkpoint after failures (not restart from scratch)
- Circuit Breakers - Fault tolerance patterns that prevent cascading failures by failing fast when services degrade
- Fallback Paths - Multi-tier fallback strategies (GPT-4 → GPT-3.5 → Rules → Human)
- The Reliability Stack Pattern - Separating probabilistic reasoning (LLM) from deterministic safety (guardrails)
Key Metrics: Resumability Rate >99%, Circuit Breaker Activations <10/day, Fallback Usage Rate <15%, MTTR <5 minutes
📖 Read the full Resilient Architecture guide →
Ensuring AI agents produce accurate, consistent, and trustworthy outputs.
Cognitive reliability addresses the correctness problem - ensuring outputs are grounded, validated, and trustworthy:
- Self-Reflection & Correction - Chain-of-thought with reflection, multi-agent debate for high-stakes decisions
- Structured Outputs - JSON schema validation, forced choice enums, regex-constrained generation
- Human-in-the-Loop (HITL) Protocols - Confidence-based escalation with design patterns to reduce HITL over time through active learning
- Drift Detection - Input drift (distribution changes), output drift (confidence shifts), model drift (version changes)
Key Metrics: Hallucination Rate <0.1%, Groundedness >95%, HITL Rate <10%, Confidence Calibration within 10%
📖 Read the full Cognitive Reliability guide →
Moving from "vibes-based" development to rigorous testing and continuous improvement.
Quality & Lifecycle practices define how to test, deploy, and continuously improve AI systems:
- Evals-Driven Deployments - CI/CD gates with golden datasets, staged rollouts (canary → gradual → full), automatic rollback triggers
- Golden Datasets - Curated regression suites (60% core capabilities, 30% edge cases, 10% adversarial), versioned in Git, continuously updated
- Unit Testing Agents - Tool calling tests, prompt adherence tests, synthetic data tests
- Online vs Offline Evals - Pre-deployment regression testing (offline) + post-deployment drift detection (online)
- Feedback Loops - Production failures → HITL corrections → golden dataset updates → model retraining
Key Metrics: Golden Dataset Accuracy >95%, Deployment Success Rate >90%, User Satisfaction >80%, Feedback Loop Latency <7 days
📖 Read the full Quality & Lifecycle guide →
Protecting systems, data, and users from risks introduced by autonomous agents.
Security for AI agents differs from traditional software-agents are autonomous decision-makers that can be manipulated to exceed intended authority:
- Just-in-Time (JIT) Privilege Access - Scoped tokens (action + resourceId) with automatic expiration (<5 minutes), step-up authentication for high-risk actions
- Audit Logs for Internal Thinking - Logging reasoning (Chain of Thought), not just inputs/outputs; structured logs for incident investigation
- Guardrails - Deterministic hard stops at three layers: input guardrails (prompt injection detection, PII redaction), output guardrails (sensitive data leakage prevention), action guardrails (rate limits, monetary limits)
- Prompt Injection Defenses - Instruction hierarchy, input sanitization, multi-model validation, sandboxing
- Data Privacy in Context Windows - Context isolation per session, PII redaction, ephemeral context for sensitive data, encryption at rest, GDPR compliance
Key Metrics: Prompt Injection Attempts <10/day, Jailbreak Success Rate <0.1%, PII Leakage Incidents 0, MTTD <5 minutes
📖 Read the full Security guide →
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 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, quality budget tracking, performance target compliance monitoring
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 →
New to AIRE? Start with the Getting Started Guide → for a step-by-step adoption roadmap:
- Phase 1 (Week 1-2): Assess current state, measure baseline metrics
- Phase 2 (Month 1): Quick wins - golden dataset, guardrails, audit logging
- Phase 3 (Month 2-3): Foundation - circuit breakers, state persistence, CI/CD evals
- Phase 4 (Month 4-6): Maturity - feedback loops, drift detection, JIT access
- Phase 5 (Month 6+): Excellence - hallucination rate <0.1%, HITL rate <10%, uptime 99.9%+
Want to dive deep? Explore the complete documentation →
This standard evolves through continuous dialogue with engineering teams operating AI systems in production. We conduct ongoing interviews with practitioners to surface new failure modes, validate emerging patterns, and refine existing guidance.
Are you running Agents in production? We are actively seeking contributors to share their architectural decisions, operational challenges, and reliability wins.
Why Contribute? You get to shape the future of AI reliability engineering and get recognized for your contributions.
| Benefit | Details |
|---|---|
| Shape the Standard | Your operational insights become codified best practices. Influence how the industry approaches AI reliability for years to come |
| Industry Recognition | Listed in the Contributors Registry as a contributor to the standards of AI reliability |
| Peer Network | Join a private forum of engineering leaders exchanging reliability patterns across enterprises |
| Early Access | Preview new sections and reference architectures before public release |
| Thank you gift | We will send you a gift hamper courtesy to our sponsors |
This repository contains the source files for the AIRE Standards documentation and deployment infrastructure:
.
├── 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
This standard belongs to the community.
We welcome Pull Requests (PRs) from engineers who have solved specific reliability challenges.
- See a missing pattern? Open a PR.
- Want to debate a standard? Open an Issue.
Contact nikita@exosphere.host to sponsor this work.
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to share and adapt this material for any purpose, even commercially, as long as you give appropriate credit.