# Building Agentic Systems
All Building Agentic Systems notes →1. AI Evaluation Frameworks
The metrics that actually define a good agent -- task success rate, cost per successful task, groundedness, and tool-call correctness -- why generic LLM benchmarks don't transfer to agent evaluation, and the LLM-as-judge pattern's known failure modes.
2. Benchmarks
Building a standing benchmark suite that runs against every model or prompt change, isolating a provider-side regression from one you introduced with a pinned-model control, and the composition, staleness, and cadence tradeoffs that keep the suite discriminating over time.
3. Online Evaluation
Continuously scoring live production traffic — LLM-as-judge scoring applied to real conversations, implicit feedback signals as cheaper proxies, shadow-mode comparison, and the sampling strategy that makes any of this affordable at production scale.
4. Offline Evaluation
Running a held-out golden dataset through a candidate agent version before deploy as a CI regression gate — golden dataset construction and versioning, hard-threshold versus regression-from-baseline pass criteria, and the coverage limit that makes online evaluation a necessary complement, not a redundant check.