Build blueprint
Adaptive Observability Sampler
A collector making keep/drop/escalate decisions from trace metadata and partial spans.
- Problem
- Teams either retain too many traces or miss the rare traces that explain incidents.
- Why Jev
- Sampling happens at high volume and requires a tiny typed decision per trace.
- Architecture
- Span features → anomaly/value Scores → deterministic quotas → storage tier.
- Current alternative
- Random, head-based, or static rule sampling.
- Jev advantage
- Semantically interesting traces can survive without LLM-scale cost per request.
- Unknowns
- Latency at collector scale and bias introduced by learned sampling.
Why this confidence: Excellent economics fit, but demanding throughput requirements.
This is an authored hypothesis derived from the research corpus. Nothing here demonstrates product demand, or that Jev performs well on this particular workload. Run the validation experiment before building past the MVP.
Sources
Limitations
- Latency at collector scale and bias introduced by learned sampling.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Probabilistic predicate + deterministic action PlausibleJev supplies fuzzy predicates while TypeScript, policies, and workflows execute constrained actions.
- Goblin HR ObservedA small inspectable demo that evaluates candidates for an impossible mission.
- The strongest near-term architecture is Jev as a complement and control layer around generative models. PlausibleJev produces decisions rather than prose, and Vercel exposes it through an evaluation-oriented API.
- Parallel decision matrix PlausibleOne state is evaluated against many named questions, replacing repeated prompt/parse cycles.