The AI Engineering Playbook: How to Improve Agent Quality and Control Costs

The ultimate guide to building and deploying LLM apps and agents

AI agents and LLM apps have moved past demos and into critical production workloads. Yet only 25% of AI initiatives deliver on their promised ROI. The teams that succeed treat AI quality and reliability as an engineering discipline, applying the same rigor to model behavior that they already apply to infrastructure.

This playbook shows you how to build that discipline. It turns iteration into an engineered loop, with AI observability as the connective tissue from offline evaluation through live production traffic.

Read the guide to explore:

• A lifecycle any team can adopt, whatever your stack
• Structured experiments across prompts, models, and retrieval
• Instrumentation that ties LLM behavior to your full stack
• Defense-in-depth for safety, security, and cost at scale

Get the guide.