agents

Analysis of 1,200 production LLM deployments reveals six key patterns separating successful teams from those stuck in demo mode: context engineering over prompt engineering, infrastructure-based guardrails, rigorous evaluation practices, and the recognition that software engineering fundamentals—not frontier models—remain the primary predictor of success.

Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.

Explore 419 new real-world LLMOps case studies from the ZenML database, now totaling 1,182 production implementations—from multi-agent systems to RAG.

Neptune AI is terminating its standalone SaaS solution. Switch to ZenML to track ML experiments and do much more.

In this article, you learn about the best Promptfoo alternatives that help you ship better AI agents.

Discover the 9 best prompt monitoring tools for ML and AI engineering teams.

Discover the 10 best LLM monitoring tools you can use this year.

In this article, you learn about the best LangSmith alternatives you can use for full-stack observability.

In this Langfuse vs LangSmith, we conclude which observability platforms fit your LLMs stack by comparing features, integration, and pricing.