# Exploration vs Exploitation

> Balance taking the best-known action (exploit) with trying alternatives that might be better (explore).

- **Category**: Agentic AI
- **Subcategory**: Planning & Control Flow
- **Canonical URL**: https://designpattern.fyi/patterns/exploration_exploitation/

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## Description
**Intent**: Balance taking the best-known action (exploit) with trying alternatives that might be better (explore).
**Context**: A team runs a long-lived agent that repeatedly chooses among a set of options — which tool to call, which prompt template to use, which strategy to try — and can observe an outcome signal after each choice (success, reward, user thumbs-up). Over time the agent should get better at the choice, not just freeze the first decent option in place.
**Solution**: - Pick an exploration strategy: epsilon-greedy (exploit with probability 1-ε, explore randomly otherwise), upper-confidence-bound (favour under-explored options with a UCB bonus), or Thompson sampling (sample from the posterior over option quality). - Apply the chosen strategy across tools, strategies, or prompt templates at the agent's decision points. - Track outcomes and adjust posteriors or bandit statistics after each run.


## Use Cases
- The agent chooses repeatedly among options (tools, strategies, prompts) and outcomes can be tracked.
- Pure exploitation is locking the agent into local optima.
- A strategy (epsilon-greedy, UCB, Thompson sampling) can be picked and tuned.





## Trade-offs


### Advantages

- Avoids local optima that pure exploitation would lock in.

- Improves with experience as the posterior sharpens.




### Considerations & Drawbacks

- Requires a reward signal; without one, exploration is noise.

- Strategy choice and hyper-parameter tuning are empirical.







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/exploration-exploitation/)