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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?
The enterprise question isn’t capture. It’s control.
On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?
That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.
The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.
I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.
Why Most Product Roadmaps Are Just Expensive Guesswork
We ve all been there: the engineering team ships an incredible feature, the marketing team blasts the launch, the metrics show a temporary spike in usage-and then... nothing. Silence. The feature slowly turns into product debt, and the actual value delivered to the user drops to zero.
As builders, we are constantly obsessed with shipping. We measure velocity, sprint completions, and launch dates. But somewhere along the way, we forgot to measure whether the things we build actually move the needle for our users bottom line.