Bayesian Bandit Experimentation
Replace fixed-split A/B tests with a bandit that dynamically shifts traffic toward better-performing agent variants in real time — minimizing exposure to the losers.
Intent & Description
🎯 Intent
Replace fixed-split A/B tests between agent variants with a bandit that dynamically reallocates traffic toward better-performing variants based on observed reward, bounding regret from bad variants.
📋 Context
You have multiple variants in play — two prompt templates, three model choices, two retrieval strategies. Classical A/B testing exposes many users to worse variants for the full test window. You want to learn and ship the winner faster.
💡 Solution
Treat each variant as a bandit arm. After each request, record the variant chosen and (when available) the reward (task success, satisfaction, cost). A Thompson sampler or UCB policy decides the next allocation. Run until posterior separation crosses a threshold or a request budget is exhausted; promote the winner. Surface posterior means and credible intervals in the experiment dashboard.
Real-world Use Case
- Multiple variants are live and reward can be observed online with reasonable delay.
- Exposing users to losing variants for a full fixed test window is a real cost.
- Operators want a live posterior — not a fixed test window — to make promotion decisions.
Source
📌 TL;DR
Ditch fixed A/B windows — let a Bayesian bandit shift traffic toward winners in real time so you stop burning users on clearly worse variants.
Advantages
- Regret from losing variants is bounded; allocation tracks evidence in real time.
- Many simultaneous variants can be explored without combinatorial regret.
- Operators see a live posterior and can promote early when evidence is clear.
Disadvantages
- Variants the bandit prunes early can be slow-burn winners — tune exploration carefully.
- Delayed reward complicates updates; naive bandits over-allocate to fast-responding variants.
- Optional-stopping at posterior-separation introduces bias if not disciplined.