JevLab - Calibrate confidence thresholds for agent routers

Hi Product Hunt! 👋 I’m the maker behind JevLab. 💡 Why I built this In agentic architectures, generative LLMs are often overkill—and far too slow—just to decide which capability or tool to trigger. Lightweight, sub-15ms decision engines (like TypeSafe Jev) solve the latency issue, but introduce a critical engineering dilemma: How do you set the confidence threshold? Threshold too low (< 0.60): The router guesses on ambiguous or multi-intent inputs, leading to silent misroutes and unintended tool dispatches. Threshold too high (> 0.90): Up to 90% of routine queries escalate back to expensive frontier models, completely eliminating the speed and cost benefits. Guessing thresholds directly in staging or production leads to brittle workflows. I built JevLab to give engineers an empirical, zero-execution playground to calibrate confidence gates before shipping code. 🛠️ What JevLab does JevLab lets you test routing confidence policies interactively without executing actual tool side-effects: Interactive Threshold Calibration: Drag the confidence slider (0.00 to 1.00) and immediately observe how your Auto-Adopt Rate (coverage), Error Rate, and Handoff Rate shift in real time. Five Bounded Outcomes: Evaluates strict classification boundaries (code_search, test_runner, docs_lookup, code_review, and none) with deterministic fail-closed safety. Clear Semantic Boundaries: Separates explicit rejection (none classification) from confidence-based policy escalation (handoff). Production-Ready TypeScript Export: Generate and export deterministic TypeScript routing modules with pinned schema validation and fail-closed handling ready for your codebase. 💬 Let's discuss! How do you currently handle routing and confidence gating in your AI agent pipelines? What is your team’s preferred balance between autonomous execution and human/fallback escalation? I’ll be here all day to answer questions, discuss agent architecture, and take your feedback. Thank you for checking out JevLab! 🚀