Build, evaluate, prove, release.
Engineering guidance for teams building evidence-backed assurance across real conversations, orchestration, retrieval, tool execution and release gates.
The documentation system
Choose the engineering path you need.
Each guide maps a practical part of the AI assurance lifecycle—from writing a journey to producing release evidence.
Getting Started
Agent → Journey → Evaluation → Evidence → Release
Writing Test Specs
Goal → Persona → Playbook → Invariants → Evidence
Evaluation Model
Deterministic → Semantic → Trajectory → Security → Verdict
RAG Reliability
Sources → Retrieval → Claims → Evidence → Verdict
Environments & Configuration
Agent → Environment → Secrets → Runtime → Evaluator
Integrations
Agent → Shyena → CI/CD → Observability → Release
API Reference
Client → API → Run → Evidence → Verdict
Reporting & Release Evidence
Release → Gates → Evidence → Findings → Decision
Troubleshooting
Failure → Classification → Evidence → Root Cause → Resolution
Latest thinking
Top 5 articles
Deep dives into AI assurance, evaluation strategy, testing, conversational systems and security.
DeepEval Rubric Scores: How to Make LLM Evaluation More Consistent
AI Agent Testing Is a Systems Problem, Not Just an LLM Evaluation Problem
Why Conversational AI Needs a Different Testing Model
The Problem With Green Checkmarks on Broken Conversations
AI Test Design for Business Workflows
New to AI agent assurance?
Start with the engineering guides, then use the latest insights for deeper context.
Engineering library
Insights & resources
Practical engineering articles and research resources for deeper technical context.
Content engineering
Documentation is part of the assurance system.
SAGE can research, draft, review, validate and publish technical knowledge through the same content-as-code path used for engineering changes.
See it evaluate your own agent
Bring one real scenario. We'll run it against your live conversational AI agent and walk through every judged turn with you.