Getting Started with AIRE

Who Should Use This Guide

This guide is designed for:

CTOs seeking to establish AI reliability standards across engineering teams
AI Architects responsible for designing production-grade agent systems
Engineering Leaders building or scaling AI agents from prototype to production
Platform Engineers implementing infrastructure for reliable AI deployments

If you're running AI agents in production (or planning to), this guide will help you adopt AIRE practices systematically.

Understanding Your Current State

Before adopting AIRE, assess your current AI reliability maturity:

Maturity Assessment

Capability	Level 0 (None)	Level 1 (Basic)	Level 2 (Intermediate)	Level 3 (Advanced)
Testing	Manual testing only	Some unit tests	Golden dataset exists	Offline + online evals in CI/CD
Monitoring	Basic logs	Structured logging	CoT logging	Full observability with alerts
Failure Recovery	Manual restart	Basic retries	State persistence	Circuit breakers + fallbacks
Security	None	Input validation	Guardrails	JIT access + audit logs
HITL	Ad-hoc	Queue system	Confidence-based routing	Active learning loop

Adoption Roadmap

Phase 1: Assess Current State (Week 1-2)

Goal: Understand existing AI agents and reliability pain points.

Tasks:

Inventory existing AI agents
- List all production agents (chatbots, automation, data processing)
- Document model types (GPT-4, Claude, custom)
- Identify critical vs non-critical agents
Identify reliability pain points
- What % of requests fail?
- How often does HITL intervene?
- What are top 3 user complaints?
Measure baseline metrics
- Success rate (% of successful requests)
- Hallucination rate (manual sample of 50+ outputs)
- HITL rate (% of requests needing human review)
- MTTR (mean time to recover from failures)

Deliverable: Reliability assessment report with baseline metrics

Phase 2: Quick Wins (Month 1)

Goal: Implement high-impact, low-effort improvements to demonstrate value.

2.1 Implement Golden Dataset for Critical Agent

Why: Catches regressions before deployment.

Steps:

Identify your most critical agent (highest business impact)
Collect 100 examples:
- 60 core capabilities (happy path)
- 30 edge cases (from production failures)
- 10 adversarial examples (prompt injections)
Store in Git with version control
Run offline evals weekly

Time: 1 week Impact: Prevents regressions, reduces production failures by 30-50%

2.2 Add Basic Guardrails

Why: Prevents catastrophic failures from LLM misbehavior.

Steps:

Implement input guardrails:
- Prompt injection detection (keyword matching)
- PII redaction (email, credit card, SSN)
- Rate limiting (per user)
Implement output guardrails:
- Sensitive data leakage prevention
- Length limits
Implement action guardrails:
- Monetary transaction limits
- Email rate limits

Time: 1 week

Impact: Reduces security incidents by 80%+

2.3 Set Up Audit Logging

Why: Enables incident investigation and debugging.

Steps: 1. Log all agent requests (user query, timestamp, userId) 2. Log agent reasoning (Chain of Thought) 3. Log action execution (success/failure) 4. Store logs in structured format (JSON) 5. Define retention policy (30-90 days)

Time: 3 days

Impact: Reduces MTTR by 50%+

Phase 3: Foundation (Month 2-3)

Goal: Build core infrastructure for reliability.

3.1 Deploy Circuit Breakers

Why: Prevents cascading failures.

Steps: 1. Identify external dependencies (LLM APIs, databases, external APIs) 2. Implement circuit breakers for each dependency 3. Configure failure thresholds (5 failures = open) 4. Add fallback paths (GPT-4 → GPT-3.5 → Human)

Time: 1 week

Impact: Improves system uptime by 2-3 nines

3.2 Implement State Persistence

Why: Enables workflow resumption after failures.

Steps: 1. Choose state store (Redis, PostgreSQL, DynamoDB) 2. Implement checkpointing (save state after each step) 3. Add workflow resumption logic 4. Test failure recovery

Time: 2 weeks Impact: Eliminates expensive LLM recomputations

3.3 Run Offline Evals in CI/CD

Why: Blocks bad deployments automatically.

Steps:

Integrate offline evals into CI/CD pipeline
Set quality gates (accuracy >95%, hallucination rate <0.1%)
Block deployment if evals fail
Alert team on failures

Time: 1 week

Impact: Prevents 90%+ of regressions from reaching production

Phase 4: Maturity (Month 4-6)

Goal: Achieve production-grade reliability.

4.1 Build Feedback Loops

Why: System improves continuously from production failures.

Steps:

Collect production failures automatically
Add HITL corrections to golden dataset weekly
Retrain model monthly on feedback
Measure improvement (HITL rate should decrease)

Time: 3 weeks

Impact: Reduces HITL rate by 50% over 6 months

4.2 Implement Drift Detection

Why: Catches silent performance degradation.

Steps:

Set up input drift monitoring (embedding divergence)
Set up output drift monitoring (confidence distribution)
Configure alerts (drift threshold: 0.3)
Create drift response playbook

Time: 1 week

Impact: Detects issues before users complain

4.3 Deploy JIT Privilege Access

Why: Minimizes blast radius of security incidents.

Steps:

Replace master API keys with scoped tokens
Implement JIT token generation (5-minute expiry)
Add step-up authentication for high-risk actions
Log all privilege requests

Time: 2 weeks

Impact: Reduces security risk by 90%+

Phase 5: Excellence (Month 6+)

Goal: Achieve industry-leading reliability.

Targets:

Hallucination Rate: <0.1%
HITL Rate: <10%
System Uptime: 99.9%+
Deployment Success Rate: >95%
MTTR: <5 minutes

Practices:

Quarterly golden dataset reviews
Monthly model retraining
Quarterly red team security testing
Continuous online evals
Full observability with real-time dashboards

Implementation Priorities

Decision Tree: Where to Start?

Is your agent in production?
├─ No: Start with Phase 2.1 (Golden Dataset)
└─ Yes: Has it caused incidents?
    ├─ No: Start with Phase 2 (Quick Wins)
    └─ Yes: What type?
        ├─ Security: Phase 2.2 (Guardrails) + Phase 4.3 (JIT Access)
        ├─ Failures: Phase 3.1 (Circuit Breakers) + Phase 3.2 (State)
        └─ Quality: Phase 2.1 (Golden Dataset) + Phase 3.3 (Offline Evals)

Priority by Agent Type

Agent Type	Priority 1	Priority 2	Priority 3
Customer Support Bot	Guardrails	Golden Dataset	HITL Protocols
Data Processing Agent	State Persistence	Circuit Breakers	Drift Detection
Code Generation Agent	Golden Dataset	Self-Reflection	Offline Evals
Financial Agent	JIT Access	Guardrails	Audit Logging

Success Metrics

Track these metrics to measure AIRE adoption progress:

Leading Indicators (Predictive)

Golden Dataset Coverage: % of agents with golden datasets
CI/CD Eval Integration: % of deployments with eval gates
Guardrail Coverage: % of agents with input/output/action guardrails

Lagging Indicators (Outcomes)

Incident Reduction: % decrease in production incidents
MTTR Improvement: % decrease in mean time to recovery
HITL Reduction: % decrease in human escalation rate
User Satisfaction: % increase in positive feedback

Common Challenges & Solutions

Challenge 1: "We don't have time to build golden datasets"

Solution: Start small (20 examples), grow iteratively. Add 5-10 examples per week from production failures.

Challenge 2: "Offline evals slow down our deployment velocity"

Solution: Run evals in parallel (5-10 minutes). Benefits (fewer production incidents) outweigh cost.

Challenge 3: "Our LLM outputs are too subjective to test"

Solution: Use semantic similarity matching (80%+ similarity = pass). Not binary, but better than nothing.

Challenge 4: "We can't afford downtime to implement these changes"

Solution: Deploy incrementally. Start with new agents, gradually migrate legacy systems.

Challenge 5: "Management doesn't prioritize reliability"

Solution: Quantify cost of unreliability (incident cost × incident rate). Present ROI case.

Next Steps

Assess your current state using the maturity assessment above
Choose your starting phase based on current maturity level
Pick one pilot agent (critical but not too complex)
Implement Phase 2 quick wins (golden dataset + guardrails + logging)
Measure improvement (baseline → post-implementation metrics)
Expand to more agents using lessons learned

Resources

Pillar 1: Resilient Architecture - Start here for infrastructure patterns
Pillar 2: Cognitive Reliability - Start here for output quality
Pillar 3: Quality & Lifecycle - Start here for testing and deployment
Pillar 4: Security - Start here for adversarial robustness

This guide is part of the AI Reliability Engineering (AIRE) Standards. Licensed under the AIRE Standards License v1.0.