AI Engineer World's Fair Highlights: Memory and Continual Learning for Agents
Stefania_druga · x · 2026-08-13
At the AI Engineer World's Fair 2026, the 'Memory & Continual Learning Track' featured insights from multiple experts.
The core thesis is that while we have scaled intelligence, we currently only have the 'world's smartest novice.' To evolve into experts, the track covered several key technical directions:
- Evaluating Continual Learning: Moving beyond static intelligence (UC Berkeley)
- Memory Harnesses: For long-running research agents (Stefania Druga)
- Scaling Compute on Context (Engram)
- Gradient-Free Continual Learning (Adaption Labs)
- Data Mining for Agents: Treating agent improvement as a data problem (LangChain)
- Enterprise Integration: Bringing continual learning into production (Applied Compute)
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