Build blueprint
Agent Model Router
A drop-in router that selects model, reasoning effort, and fallback policy per step.
- Problem
- Agents routinely send easy steps to expensive reasoning models and hard steps to models that fail.
- Why Jev
- Routing is a repeated bounded choice plus confidence score.
- Architecture
- Request state → Choice(model) + Score(complexity) → route → outcome logging → offline evaluation.
- Current alternative
- Regex rules, hand-tuned heuristics, or another full LLM call.
- Jev advantage
- Routing overhead may remain small enough to apply before every inference.
- Unknowns
- Whether the router generalizes to unseen tasks and remains cheaper after retries.
Why this confidence: Routing is repeatedly cited in the source material and maps naturally to Choice.
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
- Whether the router generalizes to unseen tasks and remains cheaper after retries.
- This is a research hypothesis, not evidence of product demand or Jev performance in this workflow.
Supporting research
- Cascade router PlausibleA cheap decision chooses whether to use rules, a small model, a premium model, a specialist, or a human.
- Confidence-gated agent control loops Authored HypothesisUse Jev repeatedly to route, verify, and decide whether an agent should continue or escalate.
- Routing, classification, verification, and workflow control are the dominant early mental models. PlausibleThose categories recur in the collected launch discussion and align with the documented output primitives.
- Confidence gate PlausibleAutomation proceeds above a threshold; ambiguous cases go to another check or a person.