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?
Replies
i like the idea of “detection should be conservative” 🔥 False positives in provenance systems can destroy trust very quickly.
@henry_lindsey Prompt → code linkage inside PR reviews feels especially valuable. That creates much better historical context around why changes happened.
Lineage Lens
@henry_lindsey @lakeesha_weatherwax That linkage ended up feeling more useful than I expected too. Seeing prompt, model context, and resulting code changes directly inside the PR flow makes reviews less about “what changed?” and more about “why did this change happen?”
It turns provenance into operational context instead of just historical logging.
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.
The adapter approach sounds thughtfully desighned 👀 Low-friction extensibility is usually what determines whether internal teams actually adopt governance tooling.
@deangelo_hinkle The evidence-based detection model makes sense 😅 Attribution without explainability would probably become messy in larger engineering teams.
Lineage Lens
@deangelo_hinkle @shawn_idrees Exactly. I think attribution systems become dangerous once they produce conclusions without exposing why the system believed the attribution was valid. In larger engineering environments, teams eventually need to audit not only the AI activity itself, but also the detection reasoning behind the provenance chain.
That’s why I wanted adapters to emit evidence alongside confidence instead of acting like opaque classifiers.
Lineage Lens
@deangelo_hinkle I’ve started believing that too. Governance tooling usually fails less from missing features and more from integration friction. If internal teams need weeks of custom plumbing before they can capture their own agents and workflows, adoption drops very quickly.
That’s why I wanted the adapter surface to stay predictable and lightweight enough that teams could extend attribution without redesigning their stack around it.
Interesting that you emphasized keeping private tooling internal instead of routing everything through a third-party vendor. I think more teams are starting to care about that.
Lineage Lens
@ali_haiider I think the conversation is definitely shifting there. Early AI tooling assumed teams would be comfortable centralizing everything through external platforms, but once provenance records start containing internal prompts, repositories, workflows, and operational decisions, organizations begin treating that data as infrastructure-level assets.
At that point self-hosting and local control stop being “enterprise features” and start becoming trust requirements.
Curious how you’re handling confidence scoring internally. Is it mostly heuristic-based right now or are you layering model assisted detection too?
Lineage Lens
@alicia_klein Right now it’s primarily heuristic-driven with evidence-weighted confidence rather than model-assisted attribution. I wanted the detection path to stay deterministic and explainable first, especially for internal tooling where teams need to understand why an attribution happened.
Long term I think model-assisted enrichment could help, but I’m cautious about introducing probabilistic interpretation too early into the canonical provenance layer.
Lineage Lens
Drop any question below !
This feels like the kind of infrastructure that becomes much more valuable once teams have dozens of AI-assisted workflows happening simulaneously.
Lineage Lens
@amard_sonal I think that’s exactly where provenance infrastructure starts becoming necessary instead of optional. A single AI workflow is manageable through human memory and informal review, but once dozens of agents, prompts, tools, and generated changes are interacting simultaneously, teams need deterministic attribution and operational history just to maintain trust in the system.
@amard_sonal @praveen62 That makes sense on the scale point. I’d still be curious where the actual floor is: at what team size does informal review stop working, and does this replace that process or sit on top of it?
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?