Multi-task imitation learning (MTIL) has shown significant potential in robotic manipulation by enabling agents to perform various tasks using a single policy. It simplifies the policy deployment and enhances the agent's adaptability across different scenarios. However, key challenges remain, such as maintaining action reliability (e.g., avoiding abnormal action sequences that deviate from nominal task trajectories) and generalizing to unseen tasks with a few expert demonstrations. To address these challenges, we introduce the Foresight-Augmented Manipulation (FoAM) policy, a novel MTIL policy that pioneers the use of multi-modal goal conditions as input and introduces a foresight augmentation in addition to the general action reconstruction. FoAM enables the agent to reason about its actions' visual consequences (foresight) and to be guided by these more expressive representations during task execution. Extensive experiments on over 100 tasks in simulation and real-world settings demonstrate that FoAM significantly enhances MTIL policy performance, outperforming state-of-the-art baselines by up to 41% in success rate. We released our simulation suites that include over 80 challenging tasks across more than 10 scenarios designed for manipulation policy training and evaluation.