#MemoryAI

[bharatpatel1061]

Lifelong Learning in AI Agents: Adapting Beyond the Dataset

Most agents are trained once and deployed, but real-world environments evolve. Lifelong learning (or continual learning) equips agents to update themselves over time without forgetting what they’ve learned.

Key techniques:

Elastic Weight Consolidation (EWC)

Replay buffers to revisit past data

Modular networks that isolate new tasks

Lifelong learning is critical for agents in dynamic systems like markets, news analysis, or scientific research. Explore evolving agent architectures on the AI agents service page.

To avoid catastrophic forgetting, schedule periodic reviews using past-case memory or synthetic data regeneration.