Knowledge hub

AI Systems Engineering

AI Systems Engineering is the discipline of turning AI tools, agents, and models into reliable, repeatable, cost-efficient engineering systems through context architecture, workflow gates, verification, and operating cadence.

Agent Context Architecture

Design what agents read, ignore, produce, and prove before engineering work starts.

Agent workflow design

Replace one long session with plan, review, implement, and verify loops.

Reliability and verification

Use review gates, behavior tests, and evidence requirements to make agent work inspectable.

Cost and context efficiency

Control token waste with routing, restart rules, cache-aware workflows, and checkpoints.

Leadership adoption

Measure AI development efficiency by outcomes, review misses, rework, and delivery evidence.

Playbooks and templates

Reusable systems for AGENTS.md, CONTEXT.md, review gates, and operating loops.