[debadyutirc]

This is awesome. Love seeing more teams investing early in observability and evals instead of treating them as an afterthought.

Your setup (LLM-assessed complexity, semantic success metrics, tool-level telemetry) hits what a lot of orgs miss, tying evaluation and observability together. Most teams stop at traces and latency, but without semantic evals, you can’t really explain or improve behavior.

We’ve seen the same pattern across production agent systems: once you layer in LLM-as-judge evals, distributed tracing, and data quality signals, debugging turns from “black box” to “explainable system.” That’s when scaling becomes viable.

Would love to hear how you’re handling drift or regression detection across those metrics. With CoAgent, we’ve been exploring automated L2–L4 eval loops (semantic, behavioral, business-value levels) and it’s been eye-opening.