Review Loops for Agents Using Real-Time Context

Agents are only as reliable as the context they retrieve, transform, and act on. In production, the hard part is not just connecting an LLM to vector stores, feature stores, tools, and tabular state. The hard part is knowing when that context was sufficient, stale, misleading, too expensive, or operationally unsafe.

This talk walks through a practical review loop for production agents that depend on real-time context. I’ll show how teams can collect representative traces, inspect retrieval and tool-use decisions, define flexible scorecards for groundedness and workflow success, combine human review with LLM-as-judge evaluators, and use the results to make release decisions. The focus is on product and engineering failure modes: agents that appear correct while using the wrong context, aggregate metrics that hide expensive edge cases, eval sets that overfit to demos, and scorecards that do not map back to real user workflows.

This is not a product pitch. The goal is to give builders a concrete operating model for evaluating context-heavy agents before and after release.