Why Lanternfish exists
AI systems should leave a trail people can understand.
Agents make consequential decisions across tools, models, and retries. Lanternfish gives the people operating them a calm, evidence-backed record of what happened and why.
Evidence-backed briefings for AI agents
Built for AI product, engineering, and operations teams that need shared context—not another wall of telemetry.
01
The gap
Traditional observability can tell a team that latency changed or a run failed. It rarely explains which context the agent saw, which alternative it rejected, or why a tool retry changed the outcome.
A metric says something moved. Evidence explains what to do next.
02
The operating idea
Lanternfish captures the calls, tools, decisions, and outcomes behind an agent run. The private control plane turns that raw activity into briefings, investigations, and memory a whole team can use.
- Calm over busy. Briefings organize attention instead of multiplying alerts.
- Evidence over assertion. Every conclusion should lead back to the run that supports it.
- Memory over repetition. What a team learns from one incident should improve the next response.
03
Light where it runs
The SDK sits inside your production code, so it stays small: the base package is the tracer and an exporter, and you add only the providers you use. It sends data to the endpoint you configure and nowhere else.
Start with one real workflow
Bring the run your team still cannot explain.
We will show you how Lanternfish turns it into a briefing with evidence, ownership, and a clear next step.