I've had people ask me what the difference is between A/B testing, multi-armed bandits, predictive/ML models, and agentic learners.
Let's use an analogy. Imagine you're investing in the stock market:
A/B Testing is like back-testing a single stock strategy (e.g., "Tech stocks beat energy in 2020, so we’ll only buy tech"). It’s rigid—once you lock in the "winner," you ignore changing markets.
Predictive Models are like relying solely on historical P/E ratios. They’re useful but backward-looking—they can’t spot new opportunities (e.g., they’d have missed the iPhone’s launch or Netflix's pivot to streaming).
Bandits are like a robo-advisor that tweaks your portfolio based on broad risk categories. It helps, but can’t adapt to your unique goals (e.g., if you're saving for a house and most other people aren't, it's not going to accommodate you).
Agentic Learners are like a seasoned hedge fund manager who tracks thousands of stocks (your message inventory), understands deeper factors, adjusts your portfolio based on your life changes, and balances safe bets with exploratory ones. E.G. "This works because of supply-chain dynamics, not sector trends" + "You’re switching jobs? Let’s dial back risk" = "Mostly blue chips, but let’s throw 5% at this new AI startup".
Agentic learners get you the evidence-based decision making of A/B tests, the contextual weighting of predictive models, and the adaptability of bandits, plus a level of personalization that none of the other three options provide. And they build context over time, which is something even an LLM has a hard time doing.
Agents learn patterns specific to individual users. That individual learning is the starting point, not a finishing touch. Agents adapt to shifts in individual preferences or behavior, and balancing consistency with smart experimentation. That’s why agents outperform other approaches. People aren't static enough, predictable enough, or enough like all the other people around them for aggregate approaches to work consistently well.
Rapid responsiveness outperforms predictive power.
