llm-agent-infra-master

Evaluating LLM agent framework choices requires structuring investigation across framework state, production readiness, tool-stack alignment, and regulatory blast radius before making a selection. This approach enforces a research-first protocol using current-state signals and measurable eval indicators to ensure production readiness.

The best way to design LLM agent evals is to prioritize evaluation over model architecture by establishing regression-backed eval sets with measurable indicators. This methodology ensures that production agents are validated through trace and eval-centric practices rather than just demo performance.

Observability helps debug failing LLM agents by implementing trace pipelines that capture detailed execution data. You can audit and fix failing agents using trace and eval-centric practices, allowing you to identify failure modes and assess production reality instead of just surface-level demo behavior.

Yes, this approach explicitly applies to multi-agent orchestration tradeoffs and production readiness assessment. It provides decision guidance by enforcing measurable eval indicators, tool alignment constraints, and regulatory exposure assessment to determine if your multi-agent system is production-ready.

You should not use a new LLM agent framework if it fails your production reality checks across eval methodology, tool-stack alignment, and regulatory blast radius. The guiding heuristic emphasizes that frameworks are temporary, meaning production readiness and eval support matter more than framework novelty.