AI Solution Design Best Practices: Balancing Determinism and AutonomyHow to build high-trust AI systems that wrap LLMs in engineering guardrails.
Master AI solution design best practices for 2026. Learn how to wrap probabilistic AI models in deterministic software guardrails for enterprise-level safety.
AI Workflows vs AI Agents: Stop Overengineering Your AI SystemsWhen deterministic pipelines outperform autonomous agents—and when they don’t
AI workflows and AI agents solve very different problems. This article breaks down deterministic AI workflows versus non-deterministic AI agents and gives you a clear decision framework to avoid overengineering your AI architecture.
CAP theorem analogies for AI engineers: a complete mapping from distributed systems to AI architectureEvery CAP concept translated and their counterparts in model behaviour, RAG design, and agentic systems
Discover how the CAP theorem translates to AI engineering. Learn to balance consistency, availability, and partition tolerance in LLMs, RAG, and agentic systems.
Proprietary vs Open LLMs: Choosing the Right Foundation for Real-World AI WorkflowsA pragmatic engineering guide to building scalable AI applications with closed and open large language models
A no-nonsense comparison of proprietary and open large language models, focusing on real AI workflows, cost, control, scalability, and long-term architectural trade-offs for production AI applications.