FIELD GUIDE / AI ENGINEERING
Practical resources for dependable GenAI systems.
A curated starting point for engineers working on retrieval, agents, evaluation, security, and the operational controls that make AI useful after the demo.
RAG / RETRIEVAL
Build evidence before answers.
AGENTS / CONTROL
Bound autonomy with evidence.
Bounded workflows over open loopsChoose explicit state, scoped tools, approval gates, and recovery paths.
Production agents beyond the modelDesign planning, state, memory, execution, observability, and failure handling.
Cascade failure containmentDetect how malformed tool state propagates across multi-agent pipelines.
SECURITY / RELEASE
Ship with a proof package.
Prompt injection architectureModel mixed-trust context, indirect injection, privilege separation, and residual risk.
LLM release gatesCarry quality, security, latency, and rollback evidence into deployment.
Security controlsInspect the public-safe trust boundaries and validation patterns used across the portfolio.
LINKEDIN / PUBLIC DISPATCHES
Ideas in the open.
Public-safe by design. The examples focus on reusable engineering patterns and synthetic demonstrations; they do not expose client data, internal prompts, or proprietary implementation details.