In an agentic architecture, that balance between exploration and exploitation emerges naturally from the system’s structure — no need for hand-tuned ratios.

Thompson Sampling is a convenient tool for navigating that tradeoff. Early on, when every option is uncertain, the system explores widely: flat distributions mean random draws lead to random choices. As the system gathers signal, those distributions sharpen. The same selection mechanism starts tilting behavior toward higher-confidence actions — exploration fades, exploitation grows. No hard-coded switches needed. You can always layer in an “epsilon-greedy” override to force occasional exploration — especially useful for catching preference shifts in long-tail segments.

This is all standard bandit architecture, but agentic systems diverge from basic bandits in that they estimate and sample from distributions on a per-user basis. That requires an enriched reward signal that makes use of every bit of information available, since individual user behavior is sparse by nature. Bandits aggregate wins and losses across users. Agents simulate a win/loss ratio for each interaction with each individual user, and then aggregate those ratios across interactions for each user.

Bandits focus on which message wins on average. Agentic systems focus on which message is likely to advance this user right now. The question isn’t “what’s the likelihood of conversion?” It’s “What gives me the best chance of progress for this individual?”

There are no fixed explore/exploit ratios. Agents explore almost constantly for silent users, exploit heavily when preferences are clear, and adapt fluidly as user behavior evolves. That’s what real balance looks like — not some artificial midpoint, but continuous responsiveness to real-time signals.