Added a custom agent to LineageLens in one afternoon
I've been working with LineageLens and just added a custom agent adapter so our internal CLI tool is attributed with prompts, model metadata, and confidence evidence. The registry design makes this surprisingly low-friction: implement a detect(input) that returns a NormalizedAgentContext (tool name, model, session ids, confidence, and evidence), register the adapter, then run the quickstart proxy to validate captures.
Why this matters: your team can capture private or bespoke tools without sending data to a vendor, and you get prompt → code linkage in PR reviews and dashboards. I followed the recent repo changes (custom agents landed in late May) and found the adapter API predictable: detection should be conservative, emit evidence items, and choose appropriate ordering so your specialist adapter wins over the fallback.
If you’ve extended LineageLens for an internal tool, what heuristics did you use to build confidence and avoid false positives?
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i like the idea of “detection should be conservative” 🔥 False positives in provenance systems can destroy trust very quickly.
Lineage Lens
@henry_lindsey Completely agree. A provenance system can tolerate missing attribution more than incorrect attribution. Once teams start seeing false positives, confidence in the entire lineage chain degrades very quickly.
That’s why I leaned toward conservative detection + evidence emission instead of trying to maximize capture rates aggressively.
Lineage Lens
Drop any question below !
Clean pattern. For confidence, I layer static signature checks with runtime context validation. Score evidence items, set a hard threshold, and only override fallback when overlap crosses ~85%. Log mismatches, review weekly. Keeps false positives low without vendor lock‑in.
This is the bar I'd want every platform to hit. If adding an agent takes weeks of config and API wrangling, most teams just won't bother. One afternoon means people actually experiment instead of planning to experiment forever.
But scaling is where it gets real. One agent in an afternoon is clean. Ten agents across different pipeline stages, though? That's where I'd expect things to get messy. Have you gotten there yet, or are you still in the single-agent phase?