How We Do Evals & Observability for Agentic Systems
· 7 min read
TL;DR:
LLM agentic systems fail in subtle ways. At Vertexcover Labs, we use a 5-part evaluation approach—powered by a structured logging foundation:
- Custom reporting/observability app to inspect the step-by-step agent flow (screenshots, LLM traces, code samples, step context, JSON/text blocks, costs).
- Component/agent-level tests (like unit tests) to isolate/fix one step without re-running the whole agent.
- End-to-end evals that validate the final product output while also comparing each stage to explain failures.
- Eval reporting dashboard (Airtable or similar) showing run status with linked "run → steps" tables for fast triage.
- Easy promotion of failing production runs into test cases (just use the run_id).
Foundation: a structured logging layer that makes all of the above trivial to build and maintain.