Production-Ready LLM Agents: A Comprehensive Framework for Offline Evaluation

The gap between building LLM agents and validating them in production is where most teams stumble. I've watched organizations ship sophisticated orchestration layers—routing between tools, managing context windows, chaining API calls—only to realize they have no systematic way to measure whether the agent actually improves outcomes. This framework addresses a real operational blind spot: offline evaluation lets us catch failures before users do, without requiring months of production telemetry. For data engineering teams, this means rethinking our observability stack. We need evaluation pipelines as first-class citizens alongside our data pipelines, capturing agent trajectories, decision points, and outcomes in structured formats. This isn't just ML validation; it's about instrumenting complex distributed systems. The architectural implication is significant: you'll need evaluation schemas in your feature stores, golden datasets in your data lakes, and scoring jobs integrated into your CI/CD workflows. As agent complexity increases across the industry, teams without this discipline will ship brittle systems that appear to work until they catastrophically fail on edge cases.