Agentic FinOps: Using Specs to Control the Cost of AI AutonomyReducing token waste by providing agents with a deterministic roadmap.
Lower your AI costs with spec-driven development. Discover how structured specifications prevent expensive recursive loops and optimize token usage in 2026.
CQRS in AI systems: why separating reads from writes is the mental model prompt engineers have been missingHow a battle-tested software architecture pattern maps surprisingly cleanly onto inference pipelines, prompt design, and AI system boundaries
Explore how CQRS analogies apply to AI engineering — separating retrieval from mutation logic to build more reliable, observable AI systems.
Designing Observability for AI Systems: From Prompts to PredictionsA practical guide to logging, monitoring, and debugging AI-powered applications
Explore how to design end-to-end observability for AI applications, covering prompt logging, model performance monitoring, data drift detection, and actionable alerts for production-grade AI systems.
Designing Predictable AI Systems in a Non-Deterministic WorldHow to balance control, autonomy, and reliability in AI architectures
Determinism matters in production AI. Explore how AI workflows provide control and reliability, while AI agents introduce non-determinism—and how to architect systems that balance both.
How AI Systems Make Decisions: Workflow Mechanisms Every Engineer Should UnderstandFrom rule engines to probabilistic models and feedback loops
A practical breakdown of the core decision-making mechanisms used in AI workflows, explaining how rules, heuristics, models, and feedback loops interact in real-world systems.