AI agents are gaining the ability to simulate outcomes, predicting what might happen based on current conditions and possible actions. This capability lets them test scenarios, weigh options, and guide decisions before anything is set in motion.
Modeling Possible Scenarios
Agents create virtual previews of events, exploring what could unfold from different starting points.
- A financial agent might simulate investment options, showing how a stock pick could grow or shrink over months.
- In a game, an agent could run through moves—like a chess play—predicting the opponent’s responses and the likely endgame.
- By mapping out these paths, they offer a glimpse of consequences without real-world risks.
Testing Actions in Advance
Before committing to a plan, agents experiment with actions in a safe, simulated space.
- A logistics agent could test delivery routes, factoring in weather or traffic, to find the fastest path without burning fuel first.
- An agent managing a smart home might simulate turning off lights versus adjusting the thermostat, picking the best energy-saving move.
- This trial-and-error approach refines strategies, catching flaws before they turn into costly mistakes.
Weighing Probabilities
Agents don’t just guess—they assign likelihoods to outcomes, grounding their forecasts in data.
- A health agent might predict the odds of recovery with different treatments, based on patient history and medical stats.
- In a business setting, an agent could estimate sales boosts from a marketing campaign, comparing it to doing nothing.
- By quantifying uncertainty, they help users prioritize options with the best chance of success.